capstone_r_and_py
#=================================================== # Set the current working directory #===================================================
import os
os.chdir("/Users/eklavya/projects/education/formalEducation/DataScience/DataScienceAssignments/HealthCare/Capstone/")#=================================================== # Libraries to be used #===================================================
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(tidyr)
library(data.table)##
## Attaching package: 'data.table'## The following objects are masked from 'package:dplyr':
##
## between, first, lastlibrary(corrplot)## corrplot 0.84 loadedlibrary(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinelibrary(ggthemes)import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')#=================================================== # Utility functions to be used #===================================================
# Function to replace "Not Available" to NA's
replace_NA <- function(x, y, z) {
x[which(x == y)] <- z
return(x)
}
func_numeric <- function(x) {
x <- as.numeric(x)
return(x)
}
func_rename <- function(x) {
x %>% rename_at(vars(-Provider.ID), function(y) paste0(y, "_score"))
}
# Function to scale the values.
negative_zscore <- function(i) {
return((mean(i, na.rm = T) - i) / (sd(i, na.rm = T)))
}
positive_zscore <- function(i) {
return((i - mean(i, na.rm = T)) / (sd(i, na.rm = T)))
}
#### Function to treat the outliers
treat_outliers <- function(df) {
for (colmn in 2:(ncol(df))) {
qtl = quantile(df[, colmn], probs = seq(0, 1, 0.00001), na.rm = T)
df[, colmn][which(df[, colmn] <= qtl[0.00125 * length(qtl)])] <- qtl[0.00125 * length(qtl)]
df[, colmn][which(df[, colmn] >= qtl[0.99875 * length(qtl)])] <- qtl[0.99875 * length(qtl)]
}
return(df)
}#=================================================== # 1. Readmission - Load “Readmissions and Deaths - Hospital.csv” file into read_raw #===================================================
read_rawdata <- read.csv("Readmissions and Deaths - Hospital.csv", stringsAsFactors = FALSE, na.strings = c("Not Available", "Not Applicable"))
dim(read_rawdata) ## 67452 rows and 18 columns## [1] 67452 18#[1] 67452 18
str(read_rawdata)## 'data.frame': 67452 obs. of 18 variables:
## $ Provider.ID : int 10001 10001 10001 10001 10001 10001 10001 10001 10001 10001 ...
## $ Hospital.Name : chr "SOUTHEAST ALABAMA MEDICAL CENTER" "SOUTHEAST ALABAMA MEDICAL CENTER" "SOUTHEAST ALABAMA MEDICAL CENTER" "SOUTHEAST ALABAMA MEDICAL CENTER" ...
## $ Address : chr "1108 ROSS CLARK CIRCLE" "1108 ROSS CLARK CIRCLE" "1108 ROSS CLARK CIRCLE" "1108 ROSS CLARK CIRCLE" ...
## $ City : chr "DOTHAN" "DOTHAN" "DOTHAN" "DOTHAN" ...
## $ State : chr "AL" "AL" "AL" "AL" ...
## $ ZIP.Code : int 36301 36301 36301 36301 36301 36301 36301 36301 36301 36301 ...
## $ County.Name : chr "HOUSTON" "HOUSTON" "HOUSTON" "HOUSTON" ...
## $ Phone.Number : num 3.35e+09 3.35e+09 3.35e+09 3.35e+09 3.35e+09 ...
## $ Measure.Name : chr "Acute Myocardial Infarction (AMI) 30-Day Mortality Rate" "Death rate for CABG" "Death rate for chronic obstructive pulmonary disease (COPD) patients" "Heart failure (HF) 30-Day Mortality Rate" ...
## $ Measure.ID : chr "MORT_30_AMI" "MORT_30_CABG" "MORT_30_COPD" "MORT_30_HF" ...
## $ Compared.to.National: chr "No Different than the National Rate" "No Different than the National Rate" "No Different than the National Rate" "No Different than the National Rate" ...
## $ Denominator : int 733 278 586 797 599 512 781 273 707 981 ...
## $ Score : num 12.5 4.2 9.3 12.4 15.5 15.4 16.5 15.1 21.1 21.4 ...
## $ Lower.Estimate : num 10.6 2.6 7.3 10.4 13 12.8 14.6 12.3 18.7 19.2 ...
## $ Higher.Estimate : num 14.9 6.8 11.8 14.6 18.5 18.6 18.8 18.5 23.8 23.7 ...
## $ Footnote : chr "" "" "" "" ...
## $ Measure.Start.Date : chr "07/01/2012" "07/01/2012" "07/01/2012" "07/01/2012" ...
## $ Measure.End.Date : chr "06/30/2015" "06/30/2015" "06/30/2015" "06/30/2015" ...unique(read_rawdata$Score)## [1] 12.5 4.2 9.3 12.4 15.5 15.4 16.5 15.1 21.1 21.4 5.1 18.7 12.7 16.0
## [15] NA 7.6 20.8 16.7 18.0 21.9 5.7 14.9 16.4 13.4 4.1 7.1 15.6 18.2
## [29] 17.9 16.1 15.2 19.8 20.6 5.0 12.0 14.4 18.8 16.6 19.9 17.3 8.2 15.7
## [43] 19.2 23.1 13.9 3.7 7.4 13.8 12.6 17.7 15.0 19.6 15.3 11.3 16.8 9.2
## [57] 26.8 15.8 23.4 19.4 12.8 14.7 3.6 8.1 14.0 21.5 17.1 8.7 12.9 19.1
## [71] 23.0 22.4 15.9 18.3 13.5 6.4 12.2 14.3 21.3 14.8 7.7 4.5 9.1 18.1
## [85] 19.5 23.3 4.7 18.6 4.8 9.5 10.6 17.2 19.0 4.9 16.9 13.0 4.4 8.0
## [99] 12.1 23.2 5.9 10.5 21.6 16.3 2.3 18.9 23.7 7.0 9.4 20.1 21.7 8.3
## [113] 20.2 16.2 22.8 4.6 7.2 11.5 13.2 3.9 14.5 20.3 24.0 13.1 1.8 11.7
## [127] 13.7 12.3 7.5 19.7 21.0 6.7 6.6 11.4 10.7 17.4 22.2 11.2 3.1 7.8
## [141] 14.2 22.3 17.5 14.6 20.0 11.6 13.6 22.5 4.3 10.4 20.4 11.9 17.0 10.9
## [155] 13.3 17.8 5.3 14.1 18.5 22.6 8.6 20.7 20.9 3.0 6.8 6.2 22.9 7.9
## [169] 5.5 21.8 6.9 20.5 9.0 22.0 3.5 8.5 5.2 9.9 22.1 18.4 10.3 19.3
## [183] 10.0 23.6 17.6 21.2 10.1 2.7 22.7 5.4 9.7 9.6 4.0 2.8 11.8 23.8
## [197] 8.9 7.3 10.8 9.8 2.9 8.4 24.6 3.8 5.6 11.0 6.0 24.8 3.2 2.1
## [211] 24.4 11.1 23.9 2.4 3.4 23.5 24.7 26.2 8.8 24.3 25.2 26.1 24.2 24.5
## [225] 24.9 3.3 2.6 6.5 1.9 2.2 25.3 6.1 10.2 25.0 24.1 26.0 5.8 6.3
## [239] 2.5 26.3 25.9 2.0 1.4 25.5 26.4 27.0 25.4 25.7 26.7 25.1 25.8 31.3
## [253] 1.7 1.5 26.9 25.6 27.2 27.4 1.6# We will filter only those columns which are needed as per the mentor.
read_meas_list = c("READM_30_AMI", "READM_30_CABG", "READM_30_COPD", "READM_30_HF", "READM_30_HIP_KNEE", "READM_30_HOSP_WIDE", "READM_30_PN", "READM_30_STK")
read_hosp <- read_rawdata[, c(1:8)]
read_hosp <- read_hosp[!duplicated(read_hosp),]
read_meas <- read_rawdata[, c(1, 10, 13)]
read_meas$Score <- func_numeric(read_meas$Score)
read_meas <- subset(read_meas, Measure.ID %in% read_meas_list)
str(read_meas)## 'data.frame': 38544 obs. of 3 variables:
## $ Provider.ID: int 10001 10001 10001 10001 10001 10001 10001 10001 10005 10005 ...
## $ Measure.ID : chr "READM_30_AMI" "READM_30_CABG" "READM_30_COPD" "READM_30_HF" ...
## $ Score : num 16.5 15.1 21.1 21.4 5.1 15.4 18.7 12.7 16.7 NA ...read_meas_score <- spread(read_meas, Measure.ID, Score)
str(read_meas_score)## 'data.frame': 4818 obs. of 9 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ READM_30_AMI : num 16.5 16.7 16.1 NA NA 17.7 16.1 17.7 NA NA ...
## $ READM_30_CABG : num 15.1 NA 15.2 NA NA 15 NA 15.8 NA NA ...
## $ READM_30_COPD : num 21.1 18 19.8 19.9 19.2 19.6 19.2 17.9 NA 23 ...
## $ READM_30_HF : num 21.4 21.9 20.6 21.1 23.1 19.8 23.4 21.5 NA 22.4 ...
## $ READM_30_HIP_KNEE : num 5.1 5.7 5 NA NA 5.1 NA 5 NA 4.1 ...
## $ READM_30_HOSP_WIDE: num 15.4 14.9 15.4 16.6 15.7 15.3 15.3 14.7 NA 15.9 ...
## $ READM_30_PN : num 18.7 16.4 17.9 17.3 16 16.7 19.4 17.1 NA 18.3 ...
## $ READM_30_STK : num 12.7 13.4 12 12.7 NA 11.3 12.8 12.4 NA 13.5 ...read_meas_score <- func_rename(read_meas_score)
str(read_meas_score)## 'data.frame': 4818 obs. of 9 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ READM_30_AMI_score : num 16.5 16.7 16.1 NA NA 17.7 16.1 17.7 NA NA ...
## $ READM_30_CABG_score : num 15.1 NA 15.2 NA NA 15 NA 15.8 NA NA ...
## $ READM_30_COPD_score : num 21.1 18 19.8 19.9 19.2 19.6 19.2 17.9 NA 23 ...
## $ READM_30_HF_score : num 21.4 21.9 20.6 21.1 23.1 19.8 23.4 21.5 NA 22.4 ...
## $ READM_30_HIP_KNEE_score : num 5.1 5.7 5 NA NA 5.1 NA 5 NA 4.1 ...
## $ READM_30_HOSP_WIDE_score: num 15.4 14.9 15.4 16.6 15.7 15.3 15.3 14.7 NA 15.9 ...
## $ READM_30_PN_score : num 18.7 16.4 17.9 17.3 16 16.7 19.4 17.1 NA 18.3 ...
## $ READM_30_STK_score : num 12.7 13.4 12 12.7 NA 11.3 12.8 12.4 NA 13.5 ...readmission <- read_meas_score
# # We will use negative zscore scaling as high readmissions implies the Hospital is not doing well in terms of patient treatment quality.
readmission[, 2:ncol(readmission)] <- sapply(readmission[, -1], negative_zscore)
str(readmission)## 'data.frame': 4818 obs. of 9 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ READM_30_AMI_score : num 0.409 0.2 0.826 NA NA ...
## $ READM_30_CABG_score : num -0.615 NA -0.704 NA NA ...
## $ READM_30_COPD_score : num -0.8668 1.5743 0.1569 0.0782 0.6294 ...
## $ READM_30_HF_score : num 0.3704 0.0364 0.9047 0.5707 -0.765 ...
## $ READM_30_HIP_KNEE_score : num -0.884 -1.968 -0.703 NA NA ...
## $ READM_30_HOSP_WIDE_score: num 0.215 0.821 0.215 -1.237 -0.148 ...
## $ READM_30_PN_score : num -1.105 0.496 -0.549 -0.131 0.774 ...
## $ READM_30_STK_score : num -0.125 -0.777 0.528 -0.125 NA ...# Outlier treatment: According to the CMS documentation, they've performed the outlier treatment for
# measures at the 0.125th and the 99.875th percentiles
readmission <- treat_outliers(readmission)
read_master <- merge(read_hosp, readmission, by = "Provider.ID")
dim(read_master)## [1] 4818 16# [1] 4818 16
str(read_master)## 'data.frame': 4818 obs. of 16 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ Hospital.Name : chr "SOUTHEAST ALABAMA MEDICAL CENTER" "MARSHALL MEDICAL CENTER SOUTH" "ELIZA COFFEE MEMORIAL HOSPITAL" "MIZELL MEMORIAL HOSPITAL" ...
## $ Address : chr "1108 ROSS CLARK CIRCLE" "2505 U S HIGHWAY 431 NORTH" "205 MARENGO STREET" "702 N MAIN ST" ...
## $ City : chr "DOTHAN" "BOAZ" "FLORENCE" "OPP" ...
## $ State : chr "AL" "AL" "AL" "AL" ...
## $ ZIP.Code : int 36301 35957 35631 36467 36049 35235 35968 35007 35233 35660 ...
## $ County.Name : chr "HOUSTON" "MARSHALL" "LAUDERDALE" "COVINGTON" ...
## $ Phone.Number : num 3.35e+09 2.57e+09 2.57e+09 3.34e+09 3.34e+09 ...
## $ READM_30_AMI_score : num 0.409 0.2 0.826 NA NA ...
## $ READM_30_CABG_score : num -0.615 NA -0.704 NA NA ...
## $ READM_30_COPD_score : num -0.8668 1.5743 0.1569 0.0782 0.6294 ...
## $ READM_30_HF_score : num 0.3704 0.0364 0.9047 0.5707 -0.765 ...
## $ READM_30_HIP_KNEE_score : num -0.884 -1.968 -0.703 NA NA ...
## $ READM_30_HOSP_WIDE_score: num 0.215 0.821 0.215 -1.237 -0.148 ...
## $ READM_30_PN_score : num -1.105 0.496 -0.549 -0.131 0.774 ...
## $ READM_30_STK_score : num -0.125 -0.777 0.528 -0.125 NA ...write.csv(readmission, "cleaned_readmission_data.csv")#=================================================== ## 2. Mortality - Load 2 Files “Readmissions and Deaths - Hospital.csv + Complications - Hospital.csv” into morality dataframe #===================================================
mort_rawdata1 <- read_rawdata
mort_rawdata2 <- read.csv("Complications - Hospital.csv", stringsAsFactors = FALSE, na.strings = c("Not Available", "Not Applicable"))
identical(names(mort_rawdata1), names(mort_rawdata2))## [1] TRUE# [1] TRUE
mort_rawdata <- rbind(mort_rawdata1, mort_rawdata2)
mort_meas_list = c("MORT_30_AMI", "MORT_30_CABG", "MORT_30_COPD", "MORT_30_HF", "MORT_30_PN", "MORT_30_STK", "PSI_4_SURG_COMP")
mort_hosp <- mort_rawdata[, c(1:8)]
mort_hosp = mort_hosp[!duplicated(mort_hosp),]
mort_meas <- mort_rawdata[, c(1, 10, 13)]
mort_meas <- subset(mort_meas, Measure.ID %in% mort_meas_list)
mort_meas$Score <- func_numeric(mort_meas$Score)
mort_meas_score <- spread(mort_meas, Measure.ID, Score)
mort_meas_score <- func_rename(mort_meas_score)
str(mort_meas_score)## 'data.frame': 4818 obs. of 8 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ MORT_30_AMI_score : num 12.5 16 16.7 NA NA 13.9 16.8 14.7 NA 15.6 ...
## $ MORT_30_CABG_score : num 4.2 NA 4.1 NA NA 3.7 NA 3.6 NA NA ...
## $ MORT_30_COPD_score : num 9.3 7.6 7.1 9.3 8.2 7.4 9.2 8.1 NA 8.7 ...
## $ MORT_30_HF_score : num 12.4 15.5 15.6 14.4 12.7 13.8 12.5 14 NA 12.9 ...
## $ MORT_30_PN_score : num 15.5 20.8 18.2 18.8 15.7 17.9 26.8 16.1 NA 19.1 ...
## $ MORT_30_STK_score : num 15.4 15.5 17.9 16.6 NA 12.6 15.8 15.8 NA 15.4 ...
## $ PSI_4_SURG_COMP_score: num 168 179 198 NA NA ...mortality <- mort_meas_score
# Mortality indicates the death rate, higher the number worser is the hospital or provider.
# Since it is related to death rate we will use negative z-score formula.
mortality[, 2:ncol(mortality)] <- sapply(mortality[, -1], negative_zscore)
str(mortality)## 'data.frame': 4818 obs. of 8 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ MORT_30_AMI_score : num 1.25 -1.55 -2.1 NA NA ...
## $ MORT_30_CABG_score : num -0.996 NA -0.881 NA NA ...
## $ MORT_30_COPD_score : num -1.094 0.434 0.883 -1.094 -0.105 ...
## $ MORT_30_HF_score : num -0.166 -2.286 -2.355 -1.534 -0.371 ...
## $ MORT_30_PN_score : num 0.428 -2.103 -0.862 -1.148 0.332 ...
## $ MORT_30_STK_score : num -0.282 -0.342 -1.784 -1.003 NA ...
## $ PSI_4_SURG_COMP_score: num -1.71 -2.3 -3.35 NA NA ...# Outlier treatment: According to the CMS documentation, they've performed the outlier treatment for
# measures at the 0.125th and the 99.875th percentiles
mortality <- treat_outliers(mortality)
mort_master <- merge(mort_hosp, mortality, by = "Provider.ID")
dim(mort_master)## [1] 4818 15#[1] 4818 15
str(mort_master)## 'data.frame': 4818 obs. of 15 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ Hospital.Name : chr "SOUTHEAST ALABAMA MEDICAL CENTER" "MARSHALL MEDICAL CENTER SOUTH" "ELIZA COFFEE MEMORIAL HOSPITAL" "MIZELL MEMORIAL HOSPITAL" ...
## $ Address : chr "1108 ROSS CLARK CIRCLE" "2505 U S HIGHWAY 431 NORTH" "205 MARENGO STREET" "702 N MAIN ST" ...
## $ City : chr "DOTHAN" "BOAZ" "FLORENCE" "OPP" ...
## $ State : chr "AL" "AL" "AL" "AL" ...
## $ ZIP.Code : int 36301 35957 35631 36467 36049 35235 35968 35007 35233 35660 ...
## $ County.Name : chr "HOUSTON" "MARSHALL" "LAUDERDALE" "COVINGTON" ...
## $ Phone.Number : num 3.35e+09 2.57e+09 2.57e+09 3.34e+09 3.34e+09 ...
## $ MORT_30_AMI_score : num 1.25 -1.55 -2.1 NA NA ...
## $ MORT_30_CABG_score : num -0.996 NA -0.881 NA NA ...
## $ MORT_30_COPD_score : num -1.094 0.434 0.883 -1.094 -0.105 ...
## $ MORT_30_HF_score : num -0.166 -2.286 -2.355 -1.534 -0.371 ...
## $ MORT_30_PN_score : num 0.428 -2.103 -0.862 -1.148 0.332 ...
## $ MORT_30_STK_score : num -0.282 -0.342 -1.784 -1.003 NA ...
## $ PSI_4_SURG_COMP_score: num -1.71 -2.3 -3.33 NA NA ...write.csv(mortality, "cleaned_mortality_data.csv")3. Safety - Load 2 files “Healthcare Associated Infections - Hospital.csv + Complications - Hospital.csv” into safety dataframe
safe_rawdata1 <- mort_rawdata
safe_rawdata2 <- read.csv("Healthcare Associated Infections - Hospital.csv", stringsAsFactors = FALSE, na.strings = c("Not Available", "Not Applicable"))
safe_rawdata1 = safe_rawdata1[, c(1:8, 10, 13)]
safe_rawdata2 = safe_rawdata2[, c(1:8, 10, 12)]
identical(names(safe_rawdata1), names(safe_rawdata2))## [1] TRUE# [1] TRUE
safe_rawdata <- rbind(safe_rawdata1, safe_rawdata2)
safe_meas_list = c("HAI_1_SIR", "HAI_2_SIR", "HAI_3_SIR", "HAI_4_SIR", "HAI_5_SIR", "HAI_6_SIR", "COMP_HIP_KNEE", "PSI_90_SAFETY")
safe_hosp <- safe_rawdata[, c(1:8)]
safe_hosp = safe_hosp[!duplicated(safe_hosp),]
safe_meas <- safe_rawdata[, c(1, 9:10)]
safe_meas <- subset(safe_meas, Measure.ID %in% safe_meas_list)
safe_meas$Score <- func_numeric(safe_meas$Score)
safe_meas_score <- spread(safe_meas, Measure.ID, Score)
safe_meas_score <- func_rename(safe_meas_score)
safety <- safe_meas_score
# The HAI measures are related to infections contracted by the patients during their stay in the hospital
# we will negative zscore here as well
safety[, 2:ncol(safety)] <- sapply(safety[, -1], negative_zscore)
str(safety)## 'data.frame': 4818 obs. of 9 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ COMP_HIP_KNEE_score: num -1.355 0.0745 -1.355 NA NA ...
## $ HAI_1_SIR_score : num -2.351 -1.023 0.389 NA NA ...
## $ HAI_2_SIR_score : num -2.088 0.05 -0.357 1.054 NA ...
## $ HAI_3_SIR_score : num -1.135 0.723 0.818 NA NA ...
## $ HAI_4_SIR_score : num 1.02 NA NA NA NA ...
## $ HAI_5_SIR_score : num 0.647 -0.457 -0.312 NA NA ...
## $ HAI_6_SIR_score : num 0.0566 0.7984 0.5887 1.5849 0.4489 ...
## $ PSI_90_SAFETY_score: num 1.2108 0.2304 -0.1156 0.5764 -0.0579 ...# Outlier treatment: According to the CMS documentation, they've performed the outlier treatment for
# measures at the 0.125th and the 99.875th percentiles
safety <- treat_outliers(safety)
safe_master <- merge(safe_hosp, safety, "Provider.ID")
dim(safe_master)## [1] 4818 16# [1] 4818 16
write.csv(safety, "cleaned_safety_data.csv")4. Experience - Load file “HCAHPS - Hospital.csv” into experience data rame
expe_rawdata <- read.csv("HCAHPS - Hospital.csv", stringsAsFactors = FALSE, na.strings = c("Not Available", "Not Applicable"))
expe_meas_list = c("H_CLEAN_LINEAR_SCORE", "H_COMP_1_LINEAR_SCORE", "H_COMP_2_LINEAR_SCORE", "H_COMP_3_LINEAR_SCORE", "H_COMP_4_LINEAR_SCORE", "H_COMP_5_LINEAR_SCORE", "H_COMP_6_LINEAR_SCORE", "H_COMP_7_LINEAR_SCORE", "H_HSP_RATING_LINEAR_SCORE", "H_QUIET_LINEAR_SCORE", "H_RECMND_LINEAR_SCORE")
names(expe_rawdata)[names(expe_rawdata) == "HCAHPS.Question"] <- "Measure.Name"
names(expe_rawdata)[names(expe_rawdata) == "HCAHPS.Measure.ID"] <- "Measure.ID"
names(expe_rawdata)[names(expe_rawdata) == "HCAHPS.Linear.Mean.Value"] <- "Score"
expe_hosp <- expe_rawdata[, c(1:8)]
expe_hosp = expe_hosp[!duplicated(expe_hosp),]
expe_meas <- expe_rawdata[, c(1, 9, 16)]
expe_meas <- subset(expe_meas, Measure.ID %in% expe_meas_list)
expe_meas$Score <- func_numeric(expe_meas$Score)
expe_meas_score <- spread(expe_meas, Measure.ID, Score)
experience <- expe_meas_score
# It measures cleanliness, patient hospitality and doctors/staff communication,
# hospital environment etc. We will use positive zscore here
experience[, 2:ncol(experience)] <- sapply(experience[, -1], positive_zscore)
str(experience)## 'data.frame': 4818 obs. of 12 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ H_CLEAN_LINEAR_SCORE : num -0.853 -1.112 -1.112 0.442 NA ...
## $ H_COMP_1_LINEAR_SCORE : num -0.524 -0.129 -0.129 -0.129 NA ...
## $ H_COMP_2_LINEAR_SCORE : num 0.0412 0.8596 0.8596 1.678 NA ...
## $ H_COMP_3_LINEAR_SCORE : num -1.2 -0.29 -0.517 0.392 NA ...
## $ H_COMP_4_LINEAR_SCORE : num -0.606 0.167 -0.22 0.553 NA ...
## $ H_COMP_5_LINEAR_SCORE : num -0.415 0.285 -0.182 0.751 NA ...
## $ H_COMP_6_LINEAR_SCORE : num 0.0276 0.3087 -1.0964 -0.2534 NA ...
## $ H_COMP_7_LINEAR_SCORE : num 0.165 -0.183 -0.532 0.165 NA ...
## $ H_HSP_RATING_LINEAR_SCORE: num 0.0842 0.3915 -1.1451 -0.5304 NA ...
## $ H_QUIET_LINEAR_SCORE : num 0.969 0.577 0.577 1.751 NA ...
## $ H_RECMND_LINEAR_SCORE : num 0.45 0.221 -0.923 -0.465 NA ...# Outlier treatment: According to the CMS documentation, they've performed the outlier treatment for
# measures at the 0.125th and the 99.875th percentiles
experience <- treat_outliers(experience)
expe_master <- merge(expe_hosp, experience, by = "Provider.ID")
dim(expe_master)## [1] 4818 19# [1] 4818 19
write.csv(experience, "cleaned_experience_data.csv")5. Medical - Load file “Outpatient Imaging Efficiency - Hopital.csv” into medical data frame
medi_rawdata <- read.csv("Outpatient Imaging Efficiency - Hospital.csv", stringsAsFactors = FALSE, na.strings = c("Not Available", "Not Applicable"))
medi_meas_list = c("OP_10", "OP_11", "OP_13", "OP_14", "OP_8")
medi_hosp <- medi_rawdata[, c(1:8)]
medi_hosp = medi_hosp[!duplicated(medi_hosp),]
medi_meas <- medi_rawdata[, c(1, 9, 11)]
medi_meas <- subset(medi_meas, Measure.ID %in% medi_meas_list)
medi_meas$Score <- as.numeric(medi_meas$Score)
medi_meas_score <- spread(medi_meas, Measure.ID, Score)
medi_meas_score <- func_rename(medi_meas_score)
medical <- medi_meas_score
# Unecessary usage of imaging tests, lower the better. We will use the negative zscore
medical[, 2:ncol(medical)] <- sapply(medical[, -1], negative_zscore)
str(medical)## 'data.frame': 4818 obs. of 6 variables:
## $ Provider.ID: int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ OP_10_score: num 0.251 -0.423 -0.277 -1.498 0.524 ...
## $ OP_11_score: num 0.389 -1.204 -0.245 -0.502 NA ...
## $ OP_13_score: num -1.2 -0.305 2.33 NA NA ...
## $ OP_14_score: num 0.207 -0.649 -0.97 NA 1.171 ...
## $ OP_8_score : num 0.3 -0.378 -0.782 NA NA ...# Outlier treatment: According to the CMS documentation, they've performed the outlier treatment for
# measures at the 0.125th and the 99.875th percentiles
medical <- treat_outliers(medical)
medi_master <- merge(medi_hosp, medical, by = "Provider.ID")
dim(medi_master)## [1] 4818 13# [1] 4818 13
write.csv(medical, "cleaned_medical_data.csv")6. Timeliness - Load file “Timely and Effective Care - Hospital.csv” into timeliness data frame
time_rawdata <- read.csv("Timely and Effective Care - Hospital.csv", stringsAsFactors = FALSE, na.strings = c("Not Available", "Not Applicable"))
time_meas_list = c("ED_1b", "ED_2b", "OP_18b", "OP_20", "OP_21", "OP_3b", "OP_5")
time_hosp <- time_rawdata[, c(1:8)]
time_hosp = time_hosp[!duplicated(time_hosp),]
time_meas <- time_rawdata[, c(1, 10, 12)]
time_meas <- subset(time_meas, Measure.ID %in% time_meas_list)
time_meas$Score <- as.numeric(time_meas$Score)
time_meas_score <- spread(time_meas, Measure.ID, Score)
time_meas_score <- func_rename(time_meas_score)
timeliness <- time_meas_score
# All the measures in timeliness indicate the average time the patient had to wait
# before being attended by the doctors or concerned specialists. We will use negative zscore
timeliness[, 2:ncol(timeliness)] <- sapply(timeliness[, -1], negative_zscore)
str(timeliness)## 'data.frame': 4818 obs. of 8 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ ED_1b_score : num 0.0892 0.3415 0.5938 0.5743 0.9527 ...
## $ ED_2b_score : num 0.508 0.463 0.357 0.508 0.69 ...
## $ OP_18b_score: num -1.266 0.616 0.235 0.568 1.068 ...
## $ OP_20_score : num -2.414 -0.049 1.009 -0.733 -0.049 ...
## $ OP_21_score : num -2.587 -0.38 -0.267 -2.078 0.243 ...
## $ OP_3b_score : num NA NA NA NA NA ...
## $ OP_5_score : num NA -0.714 NA 0.248 NA ...# Outlier treatment: According to the CMS documentation, they've performed the outlier treatment for
# measures at the 0.125th and the 99.875th percentiles
timeliness <- treat_outliers(timeliness)
time_master <- merge(time_hosp, timeliness, by = "Provider.ID")
dim(time_master)## [1] 4818 15# [1] 4818 15
write.csv(timeliness, "cleaned_timeliness_data.csv")7. Timeliness - Load file “Timely and Effective Care - Hospital.csv” into timeliness data frame
effe_rawdata <- time_rawdata
effe_meas_list = c("CAC_3", "IMM_2", "IMM_3_OP_27_FAC_ADHPCT", "OP_22", "OP_23", "OP_29", "OP_30", "OP_4", "PC_01", "STK_4", "STK_5", "STK_6", "STK_8", "VTE_1", "VTE_2", "VTE_3", "VTE_5", "VTE_6")
effe_hosp <- effe_rawdata[, c(1:8)]
effe_hosp = effe_hosp[!duplicated(effe_hosp),]
effe_meas <- effe_rawdata[, c(1, 10, 12)]
effe_meas <- subset(effe_meas, Measure.ID %in% effe_meas_list)
effe_meas$Score <- as.numeric(effe_meas$Score)
effe_meas_score <- spread(effe_meas, Measure.ID, Score)
effe_meas_score <- func_rename(effe_meas_score)
effectiveness <- effe_meas_score
# Effectiveness has some columns for which the value higher is better, and few the score lower is better
# We will both postive and negative zscores here for the filtered columns
positive_measures <- c(2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18)
negative_measures <- c(5, 10, 19)
effectiveness[, positive_measures] <- sapply(effectiveness[, positive_measures], positive_zscore)
effectiveness[, negative_measures] <- sapply(effectiveness[, negative_measures], negative_zscore)
str(effectiveness)## 'data.frame': 4818 obs. of 19 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ CAC_3_score : num NA NA NA NA NA NA NA NA NA NA ...
## $ IMM_2_score : num 0.364 0.53 0.613 0.53 0.197 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num -0.233 -0.099 0.169 -2.11 -2.781 ...
## $ OP_22_score : num -1.201 -0.108 0.438 0.438 0.438 ...
## $ OP_23_score : num NA 0.783 NA NA NA ...
## $ OP_29_score : num NA 0.692 -0.102 -2.627 0.836 ...
## $ OP_30_score : num 0.0711 0.5002 0.3285 -3.4479 0.7148 ...
## $ OP_4_score : num NA 0.546 NA -1.232 NA ...
## $ PC_01_score : num 0.539 0.324 0.539 NA NA ...
## $ STK_4_score : num -1.23 NA NA NA NA ...
## $ STK_5_score : num -0.0249 0.1932 0.1932 0.4114 NA ...
## $ STK_6_score : num 0.446 -0.915 0.198 -3.019 NA ...
## $ STK_8_score : num -0.57 0.618 -0.296 NA NA ...
## $ VTE_1_score : num 0.334 0.178 0.412 0.334 0.489 ...
## $ VTE_2_score : num 0.381 -0.45 -1.282 0.381 NA ...
## $ VTE_3_score : num -0.278 0.819 -0.887 NA NA ...
## $ VTE_5_score : num -0.2536 0.6352 -0.0759 NA NA ...
## $ VTE_6_score : num 0.416 NA 0.416 NA NA ...# Outlier treatment: According to the CMS documentation, they've performed the outlier treatment for
# measures at the 0.125th and the 99.875th percentiles
effectiveness <- treat_outliers(effectiveness)
effe_master <- merge(effe_hosp, effectiveness, by = "Provider.ID")
dim(effe_master)## [1] 4818 26# [1] 4818 26
write.csv(effectiveness, "cleaned_effectiveness_data.csv")merge1 <- merge(read_master, mort_master)
merge2 <- merge(merge1, safe_master)
merge3 <- merge(merge2, expe_master)
merge4 <- merge(merge3, medi_master)
merge5 <- merge(merge4, time_master)
merge6 <- merge(merge5, effe_master)
str(merge6)## 'data.frame': 4818 obs. of 72 variables:
## $ Provider.ID : int 100001 100002 100006 100007 100008 100009 10001 100012 100014 100017 ...
## $ Hospital.Name : chr "UF HEALTH JACKSONVILLE" "BETHESDA HOSPITAL EAST" "ORLANDO HEALTH" "FLORIDA HOSPITAL" ...
## $ Address : chr "655 W 8TH ST" "2815 S SEACREST BLVD" "52 W UNDERWOOD ST" "601 E ROLLINS ST" ...
## $ City : chr "JACKSONVILLE" "BOYNTON BEACH" "ORLANDO" "ORLANDO" ...
## $ State : chr "FL" "FL" "FL" "FL" ...
## $ ZIP.Code : int 32209 33435 32806 32803 33176 33136 36301 33901 32170 32114 ...
## $ County.Name : chr "DUVAL" "PALM BEACH" "ORANGE" "ORANGE" ...
## $ Phone.Number : num 9.04e+09 5.62e+09 3.22e+09 4.07e+09 7.87e+09 ...
## $ READM_30_AMI_score : num -2.618 0.722 0.304 -0.948 1.348 ...
## $ READM_30_CABG_score : num -1.946 0.627 0.805 -2.39 0.716 ...
## $ READM_30_COPD_score : num -0.1581 -0.5518 0.0782 -2.9142 0.3144 ...
## $ READM_30_HF_score : num -1.967 -0.364 1.305 -2.034 -0.431 ...
## $ READM_30_HIP_KNEE_score : num -0.703 1.284 -0.342 -1.426 1.103 ...
## $ READM_30_HOSP_WIDE_score : num -1.963 0.457 -0.511 -3.658 0.821 ...
## $ READM_30_PN_score : num -1.245 0.426 0.287 -1.593 -0.479 ...
## $ READM_30_STK_score : num -1.15 1.367 -0.777 -2.175 -0.87 ...
## $ MORT_30_AMI_score : num -2.504 1.489 -0.907 -0.667 0.451 ...
## $ MORT_30_CABG_score : num -0.308 -0.193 0.495 -2.257 1.068 ...
## $ MORT_30_COPD_score : num -0.0154 -0.2851 0.8833 1.7821 1.6024 ...
## $ MORT_30_HF_score : num 1.2026 0.1765 -0.0971 0.929 0.7922 ...
## $ MORT_30_PN_score : num 1.335 0.762 0.332 0.475 1.717 ...
## $ MORT_30_STK_score : num -1.9646 0.4992 0.0185 1.7612 1.3405 ...
## $ PSI_4_SURG_COMP_score : num -1.068 1.544 -0.49 0.998 0.144 ...
## $ COMP_HIP_KNEE_score : num -0.104 1.325 0.432 0.789 1.504 ...
## $ HAI_1_SIR_score : num -3.889 0.133 0.274 0.421 -0.492 ...
## $ HAI_2_SIR_score : num -0.4762 0.0302 -0.5797 0.1117 0.0236 ...
## $ HAI_3_SIR_score : num 0.087 -0.379 0.658 -0.216 0.237 ...
## $ HAI_4_SIR_score : num -2.088 0.228 0.309 0.488 -2.129 ...
## $ HAI_5_SIR_score : num -3.193 -1.519 0.639 0.287 0.15 ...
## $ HAI_6_SIR_score : num -1.204 0.395 -0.394 0.156 0.179 ...
## $ PSI_90_SAFETY_score : num -1.557 0.346 0.288 -0.635 -1.384 ...
## $ H_CLEAN_LINEAR_SCORE : num -1.888 -1.112 0.183 0.701 0.183 ...
## $ H_COMP_1_LINEAR_SCORE : num -0.918 -0.918 -0.129 0.265 0.265 ...
## $ H_COMP_2_LINEAR_SCORE : num -0.777 -1.596 -0.368 -0.777 0.45 ...
## $ H_COMP_3_LINEAR_SCORE : num -0.9724 -1.4273 -0.29 -0.0626 -0.29 ...
## $ H_COMP_4_LINEAR_SCORE : num -1.379 -0.22 0.167 0.167 0.167 ...
## $ H_COMP_5_LINEAR_SCORE : num -0.4152 -1.3482 -0.182 0.5178 0.0513 ...
## $ H_COMP_6_LINEAR_SCORE : num -0.534 -1.939 -0.815 0.309 -0.253 ...
## $ H_COMP_7_LINEAR_SCORE : num -0.532 -0.532 0.165 0.513 0.861 ...
## $ H_HSP_RATING_LINEAR_SCORE : num -0.8378 -0.2231 0.0842 0.3915 0.3915 ...
## $ H_QUIET_LINEAR_SCORE : num -0.59675 -0.00976 0.38156 0.57722 0.38156 ...
## $ H_RECMND_LINEAR_SCORE : num -1.151 -0.236 0.221 0.679 0.908 ...
## $ OP_10_score : num -2.602 0.73 0.554 0.495 0.73 ...
## $ OP_11_score : num -2.197 0.423 0.474 0.474 0.44 ...
## $ OP_13_score : num 1.1862 NA 0.0925 -0.5537 -1.7468 ...
## $ OP_14_score : num 0.0469 -1.1843 -0.756 -0.3813 0.3681 ...
## $ OP_8_score : num 1.295 NA NA -0.349 NA ...
## $ ED_1b_score : num -1.735 0.215 -0.503 -0.629 -1.851 ...
## $ ED_2b_score : num -1.312 -0.478 -0.235 -0.281 -2.086 ...
## $ OP_18b_score : num -1.766 -0.123 -0.551 -1.075 -1.932 ...
## $ OP_20_score : num -0.609 0.76 -0.547 -0.36 -1.854 ...
## $ OP_21_score : num -0.946 -0.153 -0.55 -1.512 0.696 ...
## $ OP_3b_score : num NA NA NA NA NA NA NA NA NA NA ...
## $ OP_5_score : num -5.14 NA NA NA NA ...
## $ CAC_3_score : num NA NA 0.606 NA NA ...
## $ IMM_2_score : num 0.281 0.53 0.197 0.53 0.613 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num 0.8395 -1.5068 0.0351 -0.3672 0.1691 ...
## $ OP_22_score : num -3.387 0.438 -0.108 -0.108 -0.108 ...
## $ OP_23_score : num -0.777 NA -0.777 -2.136 NA ...
## $ OP_29_score : num 0.981 -1.184 0.764 -1.364 -1.148 ...
## $ OP_30_score : num 0.844 0.2 0.629 -0.315 0.371 ...
## $ OP_4_score : num -2.48 NA NA NA NA ...
## $ PC_01_score : num 0.539 0.539 -0.75 0.539 0.539 ...
## $ STK_4_score : num 0.153 -0.179 0.374 0.374 0.594 ...
## $ STK_5_score : num -0.243 0.1932 -0.0249 -0.0249 0.4114 ...
## $ STK_6_score : num 0.446 -0.297 0.57 0.322 0.446 ...
## $ STK_8_score : num -0.113 0.618 0.253 0.435 0.618 ...
## $ VTE_1_score : num 0.334 0.489 0.412 0.412 0.567 ...
## $ VTE_2_score : num -0.118 -0.45 0.547 0.381 0.547 ...
## $ VTE_3_score : num -0.643 0.332 0.21 -1.253 0.819 ...
## $ VTE_5_score : num 0.635 0.369 0.191 0.102 0.635 ...
## $ VTE_6_score : num 0.416 0.416 0.212 0.212 0.416 ...dim(merge6)## [1] 4818 72# [1] 4818 72
master_data_x <- merge6Raw data is ready with all required 64 measures and 8 general columns
2. Data Cleaning
## Remove duplicate Data
master_data_x = master_data_x[!duplicated(master_data_x),]
dim(master_data_x) ## No duplicates found## [1] 4818 72# [1] 4818 72
## Remove NA Values
sapply(master_data_x, function(x) sum(length(which(is.na(x)))))## Provider.ID Hospital.Name
## 0 0
## Address City
## 0 0
## State ZIP.Code
## 0 0
## County.Name Phone.Number
## 0 0
## READM_30_AMI_score READM_30_CABG_score
## 2655 3791
## READM_30_COPD_score READM_30_HF_score
## 1170 1168
## READM_30_HIP_KNEE_score READM_30_HOSP_WIDE_score
## 2087 423
## READM_30_PN_score READM_30_STK_score
## 729 2210
## MORT_30_AMI_score MORT_30_CABG_score
## 2430 3780
## MORT_30_COPD_score MORT_30_HF_score
## 1227 1200
## MORT_30_PN_score MORT_30_STK_score
## 730 2142
## PSI_4_SURG_COMP_score COMP_HIP_KNEE_score
## 3000 2104
## HAI_1_SIR_score HAI_2_SIR_score
## 2443 1929
## HAI_3_SIR_score HAI_4_SIR_score
## 2775 3962
## HAI_5_SIR_score HAI_6_SIR_score
## 2988 1546
## PSI_90_SAFETY_score H_CLEAN_LINEAR_SCORE
## 1594 1310
## H_COMP_1_LINEAR_SCORE H_COMP_2_LINEAR_SCORE
## 1310 1310
## H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE
## 1310 1310
## H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE
## 1310 1310
## H_COMP_7_LINEAR_SCORE H_HSP_RATING_LINEAR_SCORE
## 1310 1310
## H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
## 1310 1310
## OP_10_score OP_11_score
## 1189 1469
## OP_13_score OP_14_score
## 2585 2514
## OP_8_score ED_1b_score
## 3294 1222
## ED_2b_score OP_18b_score
## 1238 1234
## OP_20_score OP_21_score
## 1223 1505
## OP_3b_score OP_5_score
## 4425 2574
## CAC_3_score IMM_2_score
## 4643 1034
## IMM_3_OP_27_FAC_ADHPCT_score OP_22_score
## 711 1543
## OP_23_score OP_29_score
## 3608 2087
## OP_30_score OP_4_score
## 2191 2599
## PC_01_score STK_4_score
## 2296 3919
## STK_5_score STK_6_score
## 3276 2239
## STK_8_score VTE_1_score
## 2454 1200
## VTE_2_score VTE_3_score
## 1883 2332
## VTE_5_score VTE_6_score
## 2587 3560# Provider.ID Hospital.Name Address City State
# 0 0 0 0 0
# ZIP.Code County.Name Phone.Number READM_30_AMI_score READM_30_CABG_score
# 0 0 0 2655 3791
# READM_30_COPD_score READM_30_HF_score READM_30_HIP_KNEE_score READM_30_HOSP_WIDE_score READM_30_PN_score
# 1170 1168 2087 423 729
# READM_30_STK_score MORT_30_AMI_score MORT_30_CABG_score MORT_30_COPD_score MORT_30_HF_score
# 2210 2430 3780 1227 1200
# MORT_30_PN_score MORT_30_STK_score PSI_4_SURG_COMP_score COMP_HIP_KNEE_score HAI_1_SIR_score
# 730 2142 3000 2104 2443
# HAI_2_SIR_score HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score HAI_6_SIR_score
# 1929 2775 3962 2988 1546
# PSI_90_SAFETY_score H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE
# 1594 1310 1310 1310 1310
# H_COMP_4_LINEAR_SCORE H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE H_COMP_7_LINEAR_SCORE H_HSP_RATING_LINEAR_SCORE
# 1310 1310 1310 1310 1310
# H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE OP_10_score OP_11_score OP_13_score
# 1310 1310 1189 1469 2585
# OP_14_score OP_8_score ED_1b_score ED_2b_score OP_18b_score
# 2514 3294 1222 1238 1234
# OP_20_score OP_21_score OP_3b_score OP_5_score CAC_3_score
# 1223 1505 4425 2574 4643
# IMM_2_score IMM_3_OP_27_FAC_ADHPCT_score OP_22_score OP_23_score OP_29_score
# 1034 711 1543 3608 2087
# OP_30_score OP_4_score PC_01_score STK_4_score STK_5_score
# 2191 2599 2296 3919 3276
# STK_6_score STK_8_score VTE_1_score VTE_2_score VTE_3_score
# 2239 2454 1200 1883 2332
# VTE_5_score VTE_6_score
# 2587 3560
# >
sum(sapply(master_data_x, function(x) sum(length(which(is.na(x))))))## [1] 131127# [1] 131127
## The Score columns are having 131127 NA values, score columns are very important for our further analysis. As these are all independent(X) variables
## we will deal with the NA cleaning after merging with Dependent variable (ratings- y) in later phase
str(master_data_x)## 'data.frame': 4818 obs. of 72 variables:
## $ Provider.ID : int 100001 100002 100006 100007 100008 100009 10001 100012 100014 100017 ...
## $ Hospital.Name : chr "UF HEALTH JACKSONVILLE" "BETHESDA HOSPITAL EAST" "ORLANDO HEALTH" "FLORIDA HOSPITAL" ...
## $ Address : chr "655 W 8TH ST" "2815 S SEACREST BLVD" "52 W UNDERWOOD ST" "601 E ROLLINS ST" ...
## $ City : chr "JACKSONVILLE" "BOYNTON BEACH" "ORLANDO" "ORLANDO" ...
## $ State : chr "FL" "FL" "FL" "FL" ...
## $ ZIP.Code : int 32209 33435 32806 32803 33176 33136 36301 33901 32170 32114 ...
## $ County.Name : chr "DUVAL" "PALM BEACH" "ORANGE" "ORANGE" ...
## $ Phone.Number : num 9.04e+09 5.62e+09 3.22e+09 4.07e+09 7.87e+09 ...
## $ READM_30_AMI_score : num -2.618 0.722 0.304 -0.948 1.348 ...
## $ READM_30_CABG_score : num -1.946 0.627 0.805 -2.39 0.716 ...
## $ READM_30_COPD_score : num -0.1581 -0.5518 0.0782 -2.9142 0.3144 ...
## $ READM_30_HF_score : num -1.967 -0.364 1.305 -2.034 -0.431 ...
## $ READM_30_HIP_KNEE_score : num -0.703 1.284 -0.342 -1.426 1.103 ...
## $ READM_30_HOSP_WIDE_score : num -1.963 0.457 -0.511 -3.658 0.821 ...
## $ READM_30_PN_score : num -1.245 0.426 0.287 -1.593 -0.479 ...
## $ READM_30_STK_score : num -1.15 1.367 -0.777 -2.175 -0.87 ...
## $ MORT_30_AMI_score : num -2.504 1.489 -0.907 -0.667 0.451 ...
## $ MORT_30_CABG_score : num -0.308 -0.193 0.495 -2.257 1.068 ...
## $ MORT_30_COPD_score : num -0.0154 -0.2851 0.8833 1.7821 1.6024 ...
## $ MORT_30_HF_score : num 1.2026 0.1765 -0.0971 0.929 0.7922 ...
## $ MORT_30_PN_score : num 1.335 0.762 0.332 0.475 1.717 ...
## $ MORT_30_STK_score : num -1.9646 0.4992 0.0185 1.7612 1.3405 ...
## $ PSI_4_SURG_COMP_score : num -1.068 1.544 -0.49 0.998 0.144 ...
## $ COMP_HIP_KNEE_score : num -0.104 1.325 0.432 0.789 1.504 ...
## $ HAI_1_SIR_score : num -3.889 0.133 0.274 0.421 -0.492 ...
## $ HAI_2_SIR_score : num -0.4762 0.0302 -0.5797 0.1117 0.0236 ...
## $ HAI_3_SIR_score : num 0.087 -0.379 0.658 -0.216 0.237 ...
## $ HAI_4_SIR_score : num -2.088 0.228 0.309 0.488 -2.129 ...
## $ HAI_5_SIR_score : num -3.193 -1.519 0.639 0.287 0.15 ...
## $ HAI_6_SIR_score : num -1.204 0.395 -0.394 0.156 0.179 ...
## $ PSI_90_SAFETY_score : num -1.557 0.346 0.288 -0.635 -1.384 ...
## $ H_CLEAN_LINEAR_SCORE : num -1.888 -1.112 0.183 0.701 0.183 ...
## $ H_COMP_1_LINEAR_SCORE : num -0.918 -0.918 -0.129 0.265 0.265 ...
## $ H_COMP_2_LINEAR_SCORE : num -0.777 -1.596 -0.368 -0.777 0.45 ...
## $ H_COMP_3_LINEAR_SCORE : num -0.9724 -1.4273 -0.29 -0.0626 -0.29 ...
## $ H_COMP_4_LINEAR_SCORE : num -1.379 -0.22 0.167 0.167 0.167 ...
## $ H_COMP_5_LINEAR_SCORE : num -0.4152 -1.3482 -0.182 0.5178 0.0513 ...
## $ H_COMP_6_LINEAR_SCORE : num -0.534 -1.939 -0.815 0.309 -0.253 ...
## $ H_COMP_7_LINEAR_SCORE : num -0.532 -0.532 0.165 0.513 0.861 ...
## $ H_HSP_RATING_LINEAR_SCORE : num -0.8378 -0.2231 0.0842 0.3915 0.3915 ...
## $ H_QUIET_LINEAR_SCORE : num -0.59675 -0.00976 0.38156 0.57722 0.38156 ...
## $ H_RECMND_LINEAR_SCORE : num -1.151 -0.236 0.221 0.679 0.908 ...
## $ OP_10_score : num -2.602 0.73 0.554 0.495 0.73 ...
## $ OP_11_score : num -2.197 0.423 0.474 0.474 0.44 ...
## $ OP_13_score : num 1.1862 NA 0.0925 -0.5537 -1.7468 ...
## $ OP_14_score : num 0.0469 -1.1843 -0.756 -0.3813 0.3681 ...
## $ OP_8_score : num 1.295 NA NA -0.349 NA ...
## $ ED_1b_score : num -1.735 0.215 -0.503 -0.629 -1.851 ...
## $ ED_2b_score : num -1.312 -0.478 -0.235 -0.281 -2.086 ...
## $ OP_18b_score : num -1.766 -0.123 -0.551 -1.075 -1.932 ...
## $ OP_20_score : num -0.609 0.76 -0.547 -0.36 -1.854 ...
## $ OP_21_score : num -0.946 -0.153 -0.55 -1.512 0.696 ...
## $ OP_3b_score : num NA NA NA NA NA NA NA NA NA NA ...
## $ OP_5_score : num -5.14 NA NA NA NA ...
## $ CAC_3_score : num NA NA 0.606 NA NA ...
## $ IMM_2_score : num 0.281 0.53 0.197 0.53 0.613 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num 0.8395 -1.5068 0.0351 -0.3672 0.1691 ...
## $ OP_22_score : num -3.387 0.438 -0.108 -0.108 -0.108 ...
## $ OP_23_score : num -0.777 NA -0.777 -2.136 NA ...
## $ OP_29_score : num 0.981 -1.184 0.764 -1.364 -1.148 ...
## $ OP_30_score : num 0.844 0.2 0.629 -0.315 0.371 ...
## $ OP_4_score : num -2.48 NA NA NA NA ...
## $ PC_01_score : num 0.539 0.539 -0.75 0.539 0.539 ...
## $ STK_4_score : num 0.153 -0.179 0.374 0.374 0.594 ...
## $ STK_5_score : num -0.243 0.1932 -0.0249 -0.0249 0.4114 ...
## $ STK_6_score : num 0.446 -0.297 0.57 0.322 0.446 ...
## $ STK_8_score : num -0.113 0.618 0.253 0.435 0.618 ...
## $ VTE_1_score : num 0.334 0.489 0.412 0.412 0.567 ...
## $ VTE_2_score : num -0.118 -0.45 0.547 0.381 0.547 ...
## $ VTE_3_score : num -0.643 0.332 0.21 -1.253 0.819 ...
## $ VTE_5_score : num 0.635 0.369 0.191 0.102 0.635 ...
## $ VTE_6_score : num 0.416 0.416 0.212 0.212 0.416 ...Load Dependent Variable data from Hospital Genral Information.csv file
hospital_ratings <- read.csv("Hospital General Information.csv", stringsAsFactors = FALSE, na.strings = c("Not Available", "Not Applicable"))
dim(hospital_ratings) ## 4818 rows and 28 columns - For each hospital one row is existing## [1] 4818 28# [1] 4818 28
str(hospital_ratings)## 'data.frame': 4818 obs. of 28 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ Hospital.Name : chr "SOUTHEAST ALABAMA MEDICAL CENTER" "MARSHALL MEDICAL CENTER SOUTH" "ELIZA COFFEE MEMORIAL HOSPITAL" "MIZELL MEMORIAL HOSPITAL" ...
## $ Address : chr "1108 ROSS CLARK CIRCLE" "2505 U S HIGHWAY 431 NORTH" "205 MARENGO STREET" "702 N MAIN ST" ...
## $ City : chr "DOTHAN" "BOAZ" "FLORENCE" "OPP" ...
## $ State : chr "AL" "AL" "AL" "AL" ...
## $ ZIP.Code : int 36301 35957 35631 36467 36049 35235 35968 35007 35233 35660 ...
## $ County.Name : chr "HOUSTON" "MARSHALL" "LAUDERDALE" "COVINGTON" ...
## $ Phone.Number : num 3.35e+09 2.57e+09 2.57e+09 3.34e+09 3.34e+09 ...
## $ Hospital.Type : chr "Acute Care Hospitals" "Acute Care Hospitals" "Acute Care Hospitals" "Acute Care Hospitals" ...
## $ Hospital.Ownership : chr "Government - Hospital District or Authority" "Government - Hospital District or Authority" "Government - Hospital District or Authority" "Voluntary non-profit - Private" ...
## $ Emergency.Services : chr "Yes" "Yes" "Yes" "Yes" ...
## $ Meets.criteria.for.meaningful.use.of.EHRs : chr "Y" "Y" "Y" "Y" ...
## $ Hospital.overall.rating : int 3 3 2 3 3 2 3 3 NA 2 ...
## $ Hospital.overall.rating.footnote : chr "" "" "" "" ...
## $ Mortality.national.comparison : chr "Same as the National average" "Below the National average" "Below the National average" "Same as the National average" ...
## $ Mortality.national.comparison.footnote : chr "" "" "" "" ...
## $ Safety.of.care.national.comparison : chr "Above the National average" "Same as the National average" "Same as the National average" "Same as the National average" ...
## $ Safety.of.care.national.comparison.footnote : chr "" "" "" "" ...
## $ Readmission.national.comparison : chr "Same as the National average" "Above the National average" "Same as the National average" "Below the National average" ...
## $ Readmission.national.comparison.footnote : chr "" "" "" "" ...
## $ Patient.experience.national.comparison : chr "Below the National average" "Same as the National average" "Below the National average" "Same as the National average" ...
## $ Patient.experience.national.comparison.footnote : chr "" "" "" "" ...
## $ Effectiveness.of.care.national.comparison : chr "Same as the National average" "Same as the National average" "Same as the National average" "Same as the National average" ...
## $ Effectiveness.of.care.national.comparison.footnote : chr "" "" "" "" ...
## $ Timeliness.of.care.national.comparison : chr "Same as the National average" "Above the National average" "Above the National average" "Above the National average" ...
## $ Timeliness.of.care.national.comparison.footnote : chr "" "" "" "" ...
## $ Efficient.use.of.medical.imaging.national.comparison : chr "Same as the National average" "Below the National average" "Same as the National average" NA ...
## $ Efficient.use.of.medical.imaging.national.comparison.footnote: chr "" "" "" "Results are not available for this reporting period" ...master_data_y <- hospital_ratings
sum(is.na(hospital_ratings$Hospital.overall.rating)) ## 321 records having NA values## [1] 1170# [1] 1170
# We need only the Provide.ID and Hospital.overall.rating columns
master_data_y <- master_data_y[, c(1, 13)]
str(master_data_y)## 'data.frame': 4818 obs. of 2 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ Hospital.overall.rating: int 3 3 2 3 3 2 3 3 NA 2 ...dim(master_data_y) ## 4818 rows and 2 columns (Provider id , Hospital CMS rating)## [1] 4818 2# [1] 4818 2
## Merge X (independent) & Y (dependent) variables to one using Provider id
dim(master_data_x)## [1] 4818 72# [1] 4818 72
master_data <- merge(master_data_x, master_data_y, by = "Provider.ID")
dim(master_data) ## 4818 rows & 73 columns## [1] 4818 73#[1] 4818 73Data cleaning Part 2
## Remove all the record having NA in Hospital.overall.rating column
sum(is.na(master_data$Hospital.overall.rating)) ## 1170 records having NA values## [1] 1170# [1] 1170
master_data_with_na <- master_data[is.na(master_data$Hospital.overall.rating),]
master_data_without_na <- master_data[!is.na(master_data$Hospital.overall.rating),]
dim(master_data_without_na) ## 3648 rows & 73 columns (4818 total records)## [1] 3648 73# [1] 3648 73
dim(master_data_with_na) ## 1170 rows and 73 columns## [1] 1170 73# [1] 1170 73
## NA values across all coulumns
sapply(master_data_without_na, function(x) sum(is.na(x)))## Provider.ID Hospital.Name
## 0 0
## Address City
## 0 0
## State ZIP.Code
## 0 0
## County.Name Phone.Number
## 0 0
## READM_30_AMI_score READM_30_CABG_score
## 1490 2623
## READM_30_COPD_score READM_30_HF_score
## 231 207
## READM_30_HIP_KNEE_score READM_30_HOSP_WIDE_score
## 992 3
## READM_30_PN_score READM_30_STK_score
## 125 1049
## MORT_30_AMI_score MORT_30_CABG_score
## 1267 2612
## MORT_30_COPD_score MORT_30_HF_score
## 257 221
## MORT_30_PN_score MORT_30_STK_score
## 131 984
## PSI_4_SURG_COMP_score COMP_HIP_KNEE_score
## 1831 1007
## HAI_1_SIR_score HAI_2_SIR_score
## 1322 818
## HAI_3_SIR_score HAI_4_SIR_score
## 1642 2804
## HAI_5_SIR_score HAI_6_SIR_score
## 1860 497
## PSI_90_SAFETY_score H_CLEAN_LINEAR_SCORE
## 605 273
## H_COMP_1_LINEAR_SCORE H_COMP_2_LINEAR_SCORE
## 273 273
## H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE
## 273 273
## H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE
## 273 273
## H_COMP_7_LINEAR_SCORE H_HSP_RATING_LINEAR_SCORE
## 273 273
## H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
## 273 273
## OP_10_score OP_11_score
## 287 455
## OP_13_score OP_14_score
## 1429 1438
## OP_8_score ED_1b_score
## 2141 347
## ED_2b_score OP_18b_score
## 350 381
## OP_20_score OP_21_score
## 376 497
## OP_3b_score OP_5_score
## 3267 1582
## CAC_3_score IMM_2_score
## 3516 242
## IMM_3_OP_27_FAC_ADHPCT_score OP_22_score
## 149 546
## OP_23_score OP_29_score
## 2467 1008
## OP_30_score OP_4_score
## 1095 1606
## PC_01_score STK_4_score
## 1203 2762
## STK_5_score STK_6_score
## 2133 1102
## STK_8_score VTE_1_score
## 1315 327
## VTE_2_score VTE_3_score
## 778 1208
## VTE_5_score VTE_6_score
## 1458 2417
## Hospital.overall.rating
## 0dim(master_data_without_na) ## 3648 rows and 73 columns## [1] 3648 73# [1] 3648 73Data Cleaning
# Remove all columns having more than 50% NA in the dataset .. which are not going to yield any outcome
# which is close to 1824 NA values in any x measure.. will remove the measure
## The following measures having >50% of its data as NA
## READM_30_CABG - 2623 | PSI_4_SURG_COMP - 1831 | HAI_4_SIR - 2804 | HAI_5_SIR - 1860 | OP_3b - 3267 |
## STK_4 - 2762 | STK_5 - 2133 | VTE_6 - 2417
output <- names(which(sapply(master_data_without_na, function(x) sum(is.na(x)) < 1824)))
output## [1] "Provider.ID" "Hospital.Name"
## [3] "Address" "City"
## [5] "State" "ZIP.Code"
## [7] "County.Name" "Phone.Number"
## [9] "READM_30_AMI_score" "READM_30_COPD_score"
## [11] "READM_30_HF_score" "READM_30_HIP_KNEE_score"
## [13] "READM_30_HOSP_WIDE_score" "READM_30_PN_score"
## [15] "READM_30_STK_score" "MORT_30_AMI_score"
## [17] "MORT_30_COPD_score" "MORT_30_HF_score"
## [19] "MORT_30_PN_score" "MORT_30_STK_score"
## [21] "COMP_HIP_KNEE_score" "HAI_1_SIR_score"
## [23] "HAI_2_SIR_score" "HAI_3_SIR_score"
## [25] "HAI_6_SIR_score" "PSI_90_SAFETY_score"
## [27] "H_CLEAN_LINEAR_SCORE" "H_COMP_1_LINEAR_SCORE"
## [29] "H_COMP_2_LINEAR_SCORE" "H_COMP_3_LINEAR_SCORE"
## [31] "H_COMP_4_LINEAR_SCORE" "H_COMP_5_LINEAR_SCORE"
## [33] "H_COMP_6_LINEAR_SCORE" "H_COMP_7_LINEAR_SCORE"
## [35] "H_HSP_RATING_LINEAR_SCORE" "H_QUIET_LINEAR_SCORE"
## [37] "H_RECMND_LINEAR_SCORE" "OP_10_score"
## [39] "OP_11_score" "OP_13_score"
## [41] "OP_14_score" "ED_1b_score"
## [43] "ED_2b_score" "OP_18b_score"
## [45] "OP_20_score" "OP_21_score"
## [47] "OP_5_score" "IMM_2_score"
## [49] "IMM_3_OP_27_FAC_ADHPCT_score" "OP_22_score"
## [51] "OP_29_score" "OP_30_score"
## [53] "OP_4_score" "PC_01_score"
## [55] "STK_6_score" "STK_8_score"
## [57] "VTE_1_score" "VTE_2_score"
## [59] "VTE_3_score" "VTE_5_score"
## [61] "Hospital.overall.rating"master_data_without_na <- master_data_without_na[, output]
str(master_data_without_na)## 'data.frame': 3648 obs. of 61 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10019 10021 ...
## $ Hospital.Name : chr "SOUTHEAST ALABAMA MEDICAL CENTER" "MARSHALL MEDICAL CENTER SOUTH" "ELIZA COFFEE MEMORIAL HOSPITAL" "MIZELL MEMORIAL HOSPITAL" ...
## $ Address : chr "1108 ROSS CLARK CIRCLE" "2505 U S HIGHWAY 431 NORTH" "205 MARENGO STREET" "702 N MAIN ST" ...
## $ City : chr "DOTHAN" "BOAZ" "FLORENCE" "OPP" ...
## $ State : chr "AL" "AL" "AL" "AL" ...
## $ ZIP.Code : int 36301 35957 35631 36467 36049 35235 35968 35007 35660 36360 ...
## $ County.Name : chr "HOUSTON" "MARSHALL" "LAUDERDALE" "COVINGTON" ...
## $ Phone.Number : num 3.35e+09 2.57e+09 2.57e+09 3.34e+09 3.34e+09 ...
## $ READM_30_AMI_score : num 0.409 0.2 0.826 NA NA ...
## $ READM_30_COPD_score : num -0.8668 1.5743 0.1569 0.0782 0.6294 ...
## $ READM_30_HF_score : num 0.3704 0.0364 0.9047 0.5707 -0.765 ...
## $ READM_30_HIP_KNEE_score : num -0.884 -1.968 -0.703 NA NA ...
## $ READM_30_HOSP_WIDE_score : num 0.215 0.821 0.215 -1.237 -0.148 ...
## $ READM_30_PN_score : num -1.105 0.496 -0.549 -0.131 0.774 ...
## $ READM_30_STK_score : num -0.125 -0.777 0.528 -0.125 NA ...
## $ MORT_30_AMI_score : num 1.25 -1.55 -2.1 NA NA ...
## $ MORT_30_COPD_score : num -1.094 0.434 0.883 -1.094 -0.105 ...
## $ MORT_30_HF_score : num -0.166 -2.286 -2.355 -1.534 -0.371 ...
## $ MORT_30_PN_score : num 0.428 -2.103 -0.862 -1.148 0.332 ...
## $ MORT_30_STK_score : num -0.282 -0.342 -1.784 -1.003 NA ...
## $ COMP_HIP_KNEE_score : num -1.355 0.0745 -1.355 NA NA ...
## $ HAI_1_SIR_score : num -2.351 -1.023 0.389 NA NA ...
## $ HAI_2_SIR_score : num -2.088 0.05 -0.357 1.054 NA ...
## $ HAI_3_SIR_score : num -1.135 0.723 0.818 NA NA ...
## $ HAI_6_SIR_score : num 0.0566 0.7984 0.5887 1.5849 0.4489 ...
## $ PSI_90_SAFETY_score : num 1.2108 0.2304 -0.1156 0.5764 -0.0579 ...
## $ H_CLEAN_LINEAR_SCORE : num -0.853 -1.112 -1.112 0.442 NA ...
## $ H_COMP_1_LINEAR_SCORE : num -0.524 -0.129 -0.129 -0.129 NA ...
## $ H_COMP_2_LINEAR_SCORE : num 0.0412 0.8596 0.8596 1.678 NA ...
## $ H_COMP_3_LINEAR_SCORE : num -1.2 -0.29 -0.517 0.392 NA ...
## $ H_COMP_4_LINEAR_SCORE : num -0.606 0.167 -0.22 0.553 NA ...
## $ H_COMP_5_LINEAR_SCORE : num -0.415 0.285 -0.182 0.751 NA ...
## $ H_COMP_6_LINEAR_SCORE : num 0.0276 0.3087 -1.0964 -0.2534 NA ...
## $ H_COMP_7_LINEAR_SCORE : num 0.165 -0.183 -0.532 0.165 NA ...
## $ H_HSP_RATING_LINEAR_SCORE : num 0.0842 0.3915 -1.1451 -0.5304 NA ...
## $ H_QUIET_LINEAR_SCORE : num 0.969 0.577 0.577 1.751 NA ...
## $ H_RECMND_LINEAR_SCORE : num 0.45 0.221 -0.923 -0.465 NA ...
## $ OP_10_score : num 0.251 -0.423 -0.277 -1.498 0.524 ...
## $ OP_11_score : num 0.389 -1.204 -0.245 -0.502 NA ...
## $ OP_13_score : num -1.2 -0.305 2.33 NA NA ...
## $ OP_14_score : num 0.207 -0.649 -0.97 NA 1.171 ...
## $ ED_1b_score : num 0.0892 0.3415 0.5938 0.5743 0.9527 ...
## $ ED_2b_score : num 0.508 0.463 0.357 0.508 0.69 ...
## $ OP_18b_score : num -1.266 0.616 0.235 0.568 1.068 ...
## $ OP_20_score : num -2.414 -0.049 1.009 -0.733 -0.049 ...
## $ OP_21_score : num -2.587 -0.38 -0.267 -2.078 0.243 ...
## $ OP_5_score : num NA -0.714 NA 0.248 NA ...
## $ IMM_2_score : num 0.364 0.53 0.613 0.53 0.197 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num -0.233 -0.099 0.169 -2.11 -2.781 ...
## $ OP_22_score : num -1.201 -0.108 0.438 0.438 0.438 ...
## $ OP_29_score : num NA 0.692 -0.102 -2.627 0.836 ...
## $ OP_30_score : num 0.0711 0.5002 0.3285 -3.4479 0.7148 ...
## $ OP_4_score : num NA 0.546 NA -1.232 NA ...
## $ PC_01_score : num 0.539 0.324 0.539 NA NA ...
## $ STK_6_score : num 0.446 -0.915 0.198 -3.019 NA ...
## $ STK_8_score : num -0.57 0.618 -0.296 NA NA ...
## $ VTE_1_score : num 0.334 0.178 0.412 0.334 0.489 ...
## $ VTE_2_score : num 0.381 -0.45 -1.282 0.381 NA ...
## $ VTE_3_score : num -0.278 0.819 -0.887 NA NA ...
## $ VTE_5_score : num -0.2536 0.6352 -0.0759 NA NA ...
## $ Hospital.overall.rating : int 3 3 2 3 3 2 3 3 2 4 ...## Replace NA with median values of the measure
names(master_data_without_na)## [1] "Provider.ID" "Hospital.Name"
## [3] "Address" "City"
## [5] "State" "ZIP.Code"
## [7] "County.Name" "Phone.Number"
## [9] "READM_30_AMI_score" "READM_30_COPD_score"
## [11] "READM_30_HF_score" "READM_30_HIP_KNEE_score"
## [13] "READM_30_HOSP_WIDE_score" "READM_30_PN_score"
## [15] "READM_30_STK_score" "MORT_30_AMI_score"
## [17] "MORT_30_COPD_score" "MORT_30_HF_score"
## [19] "MORT_30_PN_score" "MORT_30_STK_score"
## [21] "COMP_HIP_KNEE_score" "HAI_1_SIR_score"
## [23] "HAI_2_SIR_score" "HAI_3_SIR_score"
## [25] "HAI_6_SIR_score" "PSI_90_SAFETY_score"
## [27] "H_CLEAN_LINEAR_SCORE" "H_COMP_1_LINEAR_SCORE"
## [29] "H_COMP_2_LINEAR_SCORE" "H_COMP_3_LINEAR_SCORE"
## [31] "H_COMP_4_LINEAR_SCORE" "H_COMP_5_LINEAR_SCORE"
## [33] "H_COMP_6_LINEAR_SCORE" "H_COMP_7_LINEAR_SCORE"
## [35] "H_HSP_RATING_LINEAR_SCORE" "H_QUIET_LINEAR_SCORE"
## [37] "H_RECMND_LINEAR_SCORE" "OP_10_score"
## [39] "OP_11_score" "OP_13_score"
## [41] "OP_14_score" "ED_1b_score"
## [43] "ED_2b_score" "OP_18b_score"
## [45] "OP_20_score" "OP_21_score"
## [47] "OP_5_score" "IMM_2_score"
## [49] "IMM_3_OP_27_FAC_ADHPCT_score" "OP_22_score"
## [51] "OP_29_score" "OP_30_score"
## [53] "OP_4_score" "PC_01_score"
## [55] "STK_6_score" "STK_8_score"
## [57] "VTE_1_score" "VTE_2_score"
## [59] "VTE_3_score" "VTE_5_score"
## [61] "Hospital.overall.rating"meas <- names(master_data_without_na[, 9:ncol(master_data_without_na)])
for (i in meas) {
print(i)
print(median(master_data_without_na[, i], na.rm = TRUE))
med <- median(master_data_without_na[, i], na.rm = TRUE)
master_data_without_na[i][is.na(master_data_without_na[i])] = med
}## [1] "READM_30_AMI_score"
## [1] 0.09557352
## [1] "READM_30_COPD_score"
## [1] 0.07816308
## [1] "READM_30_HF_score"
## [1] 0.03644808
## [1] "READM_30_HIP_KNEE_score"
## [1] 0.01938441
## [1] "READM_30_HOSP_WIDE_score"
## [1] 0.09434903
## [1] "READM_30_PN_score"
## [1] 0.07794659
## [1] "READM_30_STK_score"
## [1] 0.0618616
## [1] "MORT_30_AMI_score"
## [1] 0.05139874
## [1] "MORT_30_COPD_score"
## [1] 0.07443538
## [1] "MORT_30_HF_score"
## [1] 0.03968628
## [1] "MORT_30_PN_score"
## [1] 0.04560634
## [1] "MORT_30_STK_score"
## [1] 0.0785529
## [1] "COMP_HIP_KNEE_score"
## [1] 0.07446038
## [1] "HAI_1_SIR_score"
## [1] 0.1494836
## [1] "HAI_2_SIR_score"
## [1] 0.1270887
## [1] "HAI_3_SIR_score"
## [1] 0.1595961
## [1] "HAI_6_SIR_score"
## [1] 0.02362961
## [1] "PSI_90_SAFETY_score"
## [1] 0.1150839
## [1] "H_CLEAN_LINEAR_SCORE"
## [1] -0.07594687
## [1] "H_COMP_1_LINEAR_SCORE"
## [1] 0.2652014
## [1] "H_COMP_2_LINEAR_SCORE"
## [1] 0.04117737
## [1] "H_COMP_3_LINEAR_SCORE"
## [1] -0.06257106
## [1] "H_COMP_4_LINEAR_SCORE"
## [1] 0.1667196
## [1] "H_COMP_5_LINEAR_SCORE"
## [1] 0.05126318
## [1] "H_COMP_6_LINEAR_SCORE"
## [1] 0.02763704
## [1] "H_COMP_7_LINEAR_SCORE"
## [1] 0.1648826
## [1] "H_HSP_RATING_LINEAR_SCORE"
## [1] 0.08418745
## [1] "H_QUIET_LINEAR_SCORE"
## [1] -0.009760763
## [1] "H_RECMND_LINEAR_SCORE"
## [1] -0.007499389
## [1] "OP_10_score"
## [1] 0.3191332
## [1] "OP_11_score"
## [1] 0.3886441
## [1] "OP_13_score"
## [1] 0.0428105
## [1] "OP_14_score"
## [1] 0.1004377
## [1] "ED_1b_score"
## [1] 0.1571615
## [1] "ED_2b_score"
## [1] 0.2048344
## [1] "OP_18b_score"
## [1] 0.04420063
## [1] "OP_20_score"
## [1] 0.1999376
## [1] "OP_21_score"
## [1] 0.07301241
## [1] "OP_5_score"
## [1] 0.2479597
## [1] "IMM_2_score"
## [1] 0.363809
## [1] "IMM_3_OP_27_FAC_ADHPCT_score"
## [1] 0.3032114
## [1] "OP_22_score"
## [1] 0.4381978
## [1] "OP_29_score"
## [1] 0.3673624
## [1] "OP_30_score"
## [1] 0.4143775
## [1] "OP_4_score"
## [1] 0.3679448
## [1] "PC_01_score"
## [1] 0.3239793
## [1] "STK_6_score"
## [1] 0.3221672
## [1] "STK_8_score"
## [1] 0.3440135
## [1] "VTE_1_score"
## [1] 0.3337948
## [1] "VTE_2_score"
## [1] 0.3811109
## [1] "VTE_3_score"
## [1] 0.3318061
## [1] "VTE_5_score"
## [1] 0.3685471
## [1] "Hospital.overall.rating"
## [1] 3## check for NA values after replacing NA with Median
sapply(master_data_without_na, function(x) sum(is.na(x)))## Provider.ID Hospital.Name
## 0 0
## Address City
## 0 0
## State ZIP.Code
## 0 0
## County.Name Phone.Number
## 0 0
## READM_30_AMI_score READM_30_COPD_score
## 0 0
## READM_30_HF_score READM_30_HIP_KNEE_score
## 0 0
## READM_30_HOSP_WIDE_score READM_30_PN_score
## 0 0
## READM_30_STK_score MORT_30_AMI_score
## 0 0
## MORT_30_COPD_score MORT_30_HF_score
## 0 0
## MORT_30_PN_score MORT_30_STK_score
## 0 0
## COMP_HIP_KNEE_score HAI_1_SIR_score
## 0 0
## HAI_2_SIR_score HAI_3_SIR_score
## 0 0
## HAI_6_SIR_score PSI_90_SAFETY_score
## 0 0
## H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE
## 0 0
## H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE
## 0 0
## H_COMP_4_LINEAR_SCORE H_COMP_5_LINEAR_SCORE
## 0 0
## H_COMP_6_LINEAR_SCORE H_COMP_7_LINEAR_SCORE
## 0 0
## H_HSP_RATING_LINEAR_SCORE H_QUIET_LINEAR_SCORE
## 0 0
## H_RECMND_LINEAR_SCORE OP_10_score
## 0 0
## OP_11_score OP_13_score
## 0 0
## OP_14_score ED_1b_score
## 0 0
## ED_2b_score OP_18b_score
## 0 0
## OP_20_score OP_21_score
## 0 0
## OP_5_score IMM_2_score
## 0 0
## IMM_3_OP_27_FAC_ADHPCT_score OP_22_score
## 0 0
## OP_29_score OP_30_score
## 0 0
## OP_4_score PC_01_score
## 0 0
## STK_6_score STK_8_score
## 0 0
## VTE_1_score VTE_2_score
## 0 0
## VTE_3_score VTE_5_score
## 0 0
## Hospital.overall.rating
## 0# No NA's found.
dim(master_data_without_na)## [1] 3648 61# [1] 3648 61
str(master_data_without_na)## 'data.frame': 3648 obs. of 61 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10019 10021 ...
## $ Hospital.Name : chr "SOUTHEAST ALABAMA MEDICAL CENTER" "MARSHALL MEDICAL CENTER SOUTH" "ELIZA COFFEE MEMORIAL HOSPITAL" "MIZELL MEMORIAL HOSPITAL" ...
## $ Address : chr "1108 ROSS CLARK CIRCLE" "2505 U S HIGHWAY 431 NORTH" "205 MARENGO STREET" "702 N MAIN ST" ...
## $ City : chr "DOTHAN" "BOAZ" "FLORENCE" "OPP" ...
## $ State : chr "AL" "AL" "AL" "AL" ...
## $ ZIP.Code : int 36301 35957 35631 36467 36049 35235 35968 35007 35660 36360 ...
## $ County.Name : chr "HOUSTON" "MARSHALL" "LAUDERDALE" "COVINGTON" ...
## $ Phone.Number : num 3.35e+09 2.57e+09 2.57e+09 3.34e+09 3.34e+09 ...
## $ READM_30_AMI_score : num 0.4086 0.1999 0.8261 0.0956 0.0956 ...
## $ READM_30_COPD_score : num -0.8668 1.5743 0.1569 0.0782 0.6294 ...
## $ READM_30_HF_score : num 0.3704 0.0364 0.9047 0.5707 -0.765 ...
## $ READM_30_HIP_KNEE_score : num -0.884 -1.9681 -0.7033 0.0194 0.0194 ...
## $ READM_30_HOSP_WIDE_score : num 0.215 0.821 0.215 -1.237 -0.148 ...
## $ READM_30_PN_score : num -1.105 0.496 -0.549 -0.131 0.774 ...
## $ READM_30_STK_score : num -0.1245 -0.777 0.5279 -0.1245 0.0619 ...
## $ MORT_30_AMI_score : num 1.2493 -1.5457 -2.1047 0.0514 0.0514 ...
## $ MORT_30_COPD_score : num -1.094 0.434 0.883 -1.094 -0.105 ...
## $ MORT_30_HF_score : num -0.166 -2.286 -2.355 -1.534 -0.371 ...
## $ MORT_30_PN_score : num 0.428 -2.103 -0.862 -1.148 0.332 ...
## $ MORT_30_STK_score : num -0.282 -0.3421 -1.7844 -1.0031 0.0786 ...
## $ COMP_HIP_KNEE_score : num -1.355 0.0745 -1.355 0.0745 0.0745 ...
## $ HAI_1_SIR_score : num -2.351 -1.023 0.389 0.149 0.149 ...
## $ HAI_2_SIR_score : num -2.088 0.05 -0.357 1.054 0.127 ...
## $ HAI_3_SIR_score : num -1.135 0.723 0.818 0.16 0.16 ...
## $ HAI_6_SIR_score : num 0.0566 0.7984 0.5887 1.5849 0.4489 ...
## $ PSI_90_SAFETY_score : num 1.2108 0.2304 -0.1156 0.5764 -0.0579 ...
## $ H_CLEAN_LINEAR_SCORE : num -0.8527 -1.1116 -1.1116 0.4419 -0.0759 ...
## $ H_COMP_1_LINEAR_SCORE : num -0.524 -0.129 -0.129 -0.129 0.265 ...
## $ H_COMP_2_LINEAR_SCORE : num 0.0412 0.8596 0.8596 1.678 0.0412 ...
## $ H_COMP_3_LINEAR_SCORE : num -1.1999 -0.29 -0.5175 0.3923 -0.0626 ...
## $ H_COMP_4_LINEAR_SCORE : num -0.606 0.167 -0.22 0.553 0.167 ...
## $ H_COMP_5_LINEAR_SCORE : num -0.4152 0.2845 -0.182 0.751 0.0513 ...
## $ H_COMP_6_LINEAR_SCORE : num 0.0276 0.3087 -1.0964 -0.2534 0.0276 ...
## $ H_COMP_7_LINEAR_SCORE : num 0.165 -0.183 -0.532 0.165 0.165 ...
## $ H_HSP_RATING_LINEAR_SCORE : num 0.0842 0.3915 -1.1451 -0.5304 0.0842 ...
## $ H_QUIET_LINEAR_SCORE : num 0.96855 0.57722 0.57722 1.75119 -0.00976 ...
## $ H_RECMND_LINEAR_SCORE : num 0.45 0.2213 -0.9226 -0.465 -0.0075 ...
## $ OP_10_score : num 0.251 -0.423 -0.277 -1.498 0.524 ...
## $ OP_11_score : num 0.389 -1.204 -0.245 -0.502 0.389 ...
## $ OP_13_score : num -1.2 -0.3052 2.3296 0.0428 0.0428 ...
## $ OP_14_score : num 0.207 -0.649 -0.97 0.1 1.171 ...
## $ ED_1b_score : num 0.0892 0.3415 0.5938 0.5743 0.9527 ...
## $ ED_2b_score : num 0.508 0.463 0.357 0.508 0.69 ...
## $ OP_18b_score : num -1.266 0.616 0.235 0.568 1.068 ...
## $ OP_20_score : num -2.414 -0.049 1.009 -0.733 -0.049 ...
## $ OP_21_score : num -2.587 -0.38 -0.267 -2.078 0.243 ...
## $ OP_5_score : num 0.248 -0.714 0.248 0.248 0.248 ...
## $ IMM_2_score : num 0.364 0.53 0.613 0.53 0.197 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num -0.233 -0.099 0.169 -2.11 -2.781 ...
## $ OP_22_score : num -1.201 -0.108 0.438 0.438 0.438 ...
## $ OP_29_score : num 0.367 0.692 -0.102 -2.627 0.836 ...
## $ OP_30_score : num 0.0711 0.5002 0.3285 -3.4479 0.7148 ...
## $ OP_4_score : num 0.368 0.546 0.368 -1.232 0.368 ...
## $ PC_01_score : num 0.539 0.324 0.539 0.324 0.324 ...
## $ STK_6_score : num 0.446 -0.915 0.198 -3.019 0.322 ...
## $ STK_8_score : num -0.57 0.618 -0.296 0.344 0.344 ...
## $ VTE_1_score : num 0.334 0.178 0.412 0.334 0.489 ...
## $ VTE_2_score : num 0.381 -0.45 -1.282 0.381 0.381 ...
## $ VTE_3_score : num -0.278 0.819 -0.887 0.332 0.332 ...
## $ VTE_5_score : num -0.2536 0.6352 -0.0759 0.3685 0.3685 ...
## $ Hospital.overall.rating : int 3 3 2 3 3 2 3 3 2 4 ...## Remove hospital information which is not required
master_data_without_na <- master_data_without_na[, -c(2:8)]
master_data_without_na$Hospital.overall.rating <- as.factor(master_data_without_na$Hospital.overall.rating)
cleaned_master_data <- master_data_without_na
dim(cleaned_master_data)## [1] 3648 54# [1] 3648 54Perform EDA
library(Information)
# let us check the hospital_ratings dataset
summary(factor(hospital_ratings$Hospital.Type))## Acute Care Hospitals Childrens
## 3382 99
## Critical Access Hospitals
## 1337# Acute Care Hospitals Childrens Critical Access Hospitals
# 3382 99 1337
# Acute care hospitals are the highest in the dataset, followed by Critical Access Hospitals and Childrens hospitals.
# Let us verify the hospitals ratings for each type for hospitals.
table(hospital_ratings$Hospital.Type, hospital_ratings$Hospital.overall.rating)##
## 1 2 3 4 5
## Acute Care Hospitals 117 659 1426 749 110
## Childrens 0 0 0 0 0
## Critical Access Hospitals 0 25 346 215 1# 1 2 3 4 5
# Acute Care Hospitals 117 659 1426 749 110
# Childrens 0 0 0 0 0
# Critical Access Hospitals 0 25 346 215 1
# Since the Childrens hospital has no ratings, we will filter these records from the dataset.
# we will remove the Critical Access Hospitals as well, as there are no ratings for 1 and very few for 5.
hospital_ratings <- filter(hospital_ratings, Hospital.Type == "Acute Care Hospitals")
hospital_ratings$Hospital.Type <- factor(as.character(hospital_ratings$Hospital.Type))
table(hospital_ratings$Hospital.Type, hospital_ratings$Hospital.overall.rating)##
## 1 2 3 4 5
## Acute Care Hospitals 117 659 1426 749 110# 1 2 3 4 5
# Acute Care Hospitals 117 659 1426 749 110
# Let us check the Overall rating
unique(hospital_ratings$Hospital.overall.rating)## [1] 3 2 NA 4 5 1# [1] 3 2 NA 4 5 1
hospital_ratings$Hospital.overall.rating <- func_numeric(hospital_ratings$Hospital.overall.rating)# Let's check the Ownership type for the hospitals.
table(hospital_ratings$Hospital.Ownership)##
## Government - Federal
## 38
## Government - Hospital District or Authority
## 281
## Government - Local
## 183
## Government - State
## 54
## Physician
## 63
## Proprietary
## 723
## Tribal
## 4
## Voluntary non-profit - Church
## 284
## Voluntary non-profit - Other
## 369
## Voluntary non-profit - Private
## 1383# Government - Federal Government - Hospital District or Authority Government - Local
# 38 281 183
# Government - State Physician Proprietary
# 54 63 723
# Tribal Voluntary non-profit - Church Voluntary non-profit - Other
# 4 284 369
# Voluntary non-profit - Private
# 1383
# We see the Private hospitals have the data regarding the ratings in the dataset.
# Let us remove the NA's values-
hospital_ratings <- hospital_ratings[!is.na(hospital_ratings$Hospital.overall.rating),]
dim(hospital_ratings)## [1] 3061 28# [1] 3061 28Univariate analysis - Segmented:
# Let us Segment across different groups categorical variables and compare their average overall ratings
avg_by_state <- hospital_ratings %>%
group_by(State) %>%
summarise(avg_rating = mean(Hospital.overall.rating, na.rm = T), provider_count = n()) %>%
arrange(desc(avg_rating))
avg_by_state## # A tibble: 53 x 3
## State avg_rating provider_count
## <chr> <dbl> <int>
## 1 SD 4.2 15
## 2 WI 3.69 65
## 3 DE 3.67 6
## 4 ID 3.67 12
## 5 IN 3.59 80
## 6 MT 3.58 12
## 7 NH 3.54 13
## 8 KS 3.52 44
## 9 MN 3.52 48
## 10 CO 3.5 44
## # … with 43 more rowshead(avg_by_state, 10)## # A tibble: 10 x 3
## State avg_rating provider_count
## <chr> <dbl> <int>
## 1 SD 4.2 15
## 2 WI 3.69 65
## 3 DE 3.67 6
## 4 ID 3.67 12
## 5 IN 3.59 80
## 6 MT 3.58 12
## 7 NH 3.54 13
## 8 KS 3.52 44
## 9 MN 3.52 48
## 10 CO 3.5 44# A tibble: 53 x 3
# State avg_rating provider_count
# <chr> <dbl> <int>
# SD 4.2 15
# WI 3.69 65
# DE 3.67 6
# ID 3.67 12
# IN 3.59 80
# MT 3.58 12
# NH 3.54 13
# KS 3.52 44
# MN 3.52 48
# CO 3.5 44
# We see that South Dakota(SD) has the highest average of 4.2 but the frequency is not the highest.
# we see that Indiana has an average of 3.59 and has the highest frequency.
# We see that Wisconsin has an average of 3.69 and a good count of providers.graph_by_prov_state <- head(avg_by_state, 10) %>%
ggplot(aes(x = State, y = avg_rating, fill = State,
label = paste("(", round(avg_rating, 2), ",", provider_count, ")"), vjust = -2)) +
geom_bar(stat = "identity") +
geom_text(size = 2, vjust = -1) +
theme_classic() +
xlab("Hospitals by State") +
ylab("Average rating") +
ggtitle("Average Ratings by State")
graph_by_prov_state
avg_by_hosp_ownership <- hospital_ratings %>%
group_by(Hospital.Ownership) %>%
summarise(avg_rating = mean(Hospital.overall.rating, na.rm = T), hospital_count = n()) %>%
arrange(desc(avg_rating))
avg_by_hosp_ownership## # A tibble: 10 x 3
## Hospital.Ownership avg_rating hospital_count
## <chr> <dbl> <int>
## 1 Physician 4.10 31
## 2 Voluntary non-profit - Church 3.15 275
## 3 Voluntary non-profit - Other 3.11 348
## 4 Voluntary non-profit - Private 3.07 1284
## 5 Government - Hospital District or Authority 2.96 261
## 6 Government - Federal 2.93 15
## 7 Proprietary 2.91 633
## 8 Government - Local 2.79 168
## 9 Government - State 2.64 44
## 10 Tribal 2.5 2# A tibble: 10 x 3
# Hospital.Ownership avg_rating hospital_count
# <chr> <dbl> <int>
# 1 Physician 4.10 31
# 2 Voluntary non-profit - Church 3.15 275
# 3 Voluntary non-profit - Other 3.11 348
# 4 Voluntary non-profit - Private 3.07 1284
# 5 Government - Hospital District or Authority 2.96 261
# 6 Government - Federal 2.93 15
# 7 Proprietary 2.91 633
# 8 Government - Local 2.79 168
# 9 Government - State 2.64 44
# 10 Tribal 2.5 2
graph_by_hosp_owner <- avg_by_hosp_ownership %>%
ggplot(aes(x = Hospital.Ownership, y = avg_rating, fill = Hospital.Ownership,
label = paste("(", round(avg_rating, 2), ",", hospital_count, ")"), vjust = -2)) +
geom_bar(stat = "identity") +
geom_text(size = 2, vjust = -1) +
theme_classic() +
xlab("Hospitals by Onwership") +
ylab("Average rating") +
ggtitle("Average Ratings by Onwership")
graph_by_hosp_owner
# we see good average ratings for "Physician" owned hospitals and voluntary non-profit owned hospitals
# Comparitively low ratings for tribal, Government -local owned hospitals.
library(cowplot)##
## ********************************************************## Note: As of version 1.0.0, cowplot does not change the## default ggplot2 theme anymore. To recover the previous## behavior, execute:
## theme_set(theme_cowplot())## ********************************************************##
## Attaching package: 'cowplot'## The following object is masked from 'package:ggthemes':
##
## theme_mapgrid_plot <- plot_grid(graph_by_prov_state, graph_by_hosp_owner)
grid_plot
# Average of Readmission Ratings
avg_by_readmission <- hospital_ratings %>%
group_by(Readmission.national.comparison) %>%
summarise(avg_rating = mean(Hospital.overall.rating, na.rm = T), hospital_count = n()) %>%
arrange(desc(avg_rating))
avg_by_readmission## # A tibble: 4 x 3
## Readmission.national.comparison avg_rating hospital_count
## <chr> <dbl> <int>
## 1 <NA> 3.92 124
## 2 Above the National average 3.57 791
## 3 Same as the National average 3.03 1299
## 4 Below the National average 2.38 847# A tibble: 4 x 3
# Readmission.national.comparison avg_rating hospital_count
# <chr> <dbl> <int>
# 1 NA 3.92 124
# 2 Above the National average 3.57 791
# 3 Same as the National average 3.03 1299
# 4 Below the National average 2.38 847
# Here we see that the average ratings of hospitals which have above the national average for readmissions is about 3.57 and around 811 hospitals rank here
# A considerable number of hospitals have average same as national average and their average rating is around 3.12
# Those which are below the national average have an average rating around 2.38.
# Above and Below national average hospitals are similar in number with respect to the readmissions count.
graph_by_readmission <- avg_by_readmission %>%
ggplot(aes(x = Readmission.national.comparison, y = avg_rating, fill = Readmission.national.comparison,
label = paste("(", round(avg_rating, 2), ",", hospital_count, ")"), vjust = -2)) +
geom_bar(stat = "identity") +
geom_text(size = 3, vjust = -0.5) +
theme_classic() +
xlab("Hospitals by National Readmission Comparison ") +
ylab("Average rating") +
ggtitle("Average Ratings by Readmissions")
graph_by_readmission
readmission_gp <- round(avg_by_readmission$avg_rating[2] - avg_by_readmission$avg_rating[4], 2)
# Average Mortality ratings
avg_by_mortality <- hospital_ratings %>%
group_by(Mortality.national.comparison) %>%
summarise(avg_rating = mean(Hospital.overall.rating, na.rm = T), hospital_count = n()) %>%
arrange(desc(avg_rating))
avg_by_mortality## # A tibble: 4 x 3
## Mortality.national.comparison avg_rating hospital_count
## <chr> <dbl> <int>
## 1 <NA> 3.63 194
## 2 Above the National average 3.29 400
## 3 Same as the National average 3.01 2124
## 4 Below the National average 2.48 343# A tibble: 4 x 3
# Mortality.national.comparison avg_rating hospital_count
# <chr> <dbl> <int>
# 1 NA 3.63 194
# 2 Above the National average 3.29 400
# 3 Same as the National average 3.01 2124
# 4 Below the National average 2.48 343
# Excluding the NA's, around 400 hospitals have mortality rate above the national average
# A considerable number of hospitals (2124) have mortality rate same as the National average
# Few hospitals (343) have the mortality rate below the National average.
graph_by_mortality <- avg_by_mortality %>%
ggplot(aes(x = Mortality.national.comparison, y = avg_rating, fill = Mortality.national.comparison,
label = paste("(", round(avg_rating, 2), ",", hospital_count, ")"), vjust = -2)) +
geom_bar(stat = "identity") +
geom_text(size = 3, vjust = -0.5) +
theme_classic() +
xlab("Hospitals by National Morality Comparison") +
ylab("Average rating") +
ggtitle("Average Ratings by Mortality")
graph_by_mortality
mortality_gp <- round(avg_by_mortality$avg_rating[2] - avg_by_mortality$avg_rating[4], 2)
# Average safety ratings
avg_by_safety <- hospital_ratings %>%
group_by(Safety.of.care.national.comparison) %>%
summarise(avg_rating = mean(Hospital.overall.rating, na.rm = T), hospital_count = n()) %>%
arrange(desc(avg_rating))
avg_by_safety## # A tibble: 4 x 3
## Safety.of.care.national.comparison avg_rating hospital_count
## <chr> <dbl> <int>
## 1 Above the National average 3.44 804
## 2 <NA> 3.12 205
## 3 Same as the National average 3.09 1379
## 4 Below the National average 2.36 673# # A tibble: 4 x 3
# Safety.of.care.national.comparison avg_rating hospital_count
# <chr> <dbl> <int>
# 1 Above the National average 3.44 804
# 2 NA 3.12 205
# 3 Same as the National average 3.09 1379
# 4 Below the National average 2.36 673
# Here we see that the average ratings of hospitals which have above the national average for safety of care is about 3.44 and around 804 hospitals rank here
# A considerable number of hospitals have average same as national average and their average rating is around 3.09
# Those which are below the national average have an average rating around 2.36.
graph_by_safety <- avg_by_safety %>%
ggplot(aes(x = Safety.of.care.national.comparison, y = avg_rating, fill = Safety.of.care.national.comparison,
label = paste("(", round(avg_rating, 2), ",", hospital_count, ")"), vjust = -2)) +
geom_bar(stat = "identity") +
geom_text(size = 3, vjust = -0.5) +
theme_classic() +
xlab("Hospitals by National Safety of care Comparison") +
ylab("Average rating") +
ggtitle("Average Ratings by Safety")
graph_by_safety
safety_gp <- round(avg_by_safety$avg_rating[1] - avg_by_safety$avg_rating[4], 2)
# Average experience ratings
avg_by_experience <- hospital_ratings %>%
group_by(Patient.experience.national.comparison) %>%
summarise(avg_rating = mean(Hospital.overall.rating, na.rm = T), hospital_count = n()) %>%
arrange(desc(avg_rating))
avg_by_experience## # A tibble: 4 x 3
## Patient.experience.national.comparison avg_rating hospital_count
## <chr> <dbl> <int>
## 1 Above the National average 3.69 887
## 2 Same as the National average 3.11 996
## 3 <NA> 2.86 99
## 4 Below the National average 2.42 1079# # A tibble: 4 x 3
# Patient.experience.national.comparison avg_rating hospital_count
# <chr> <dbl> <int>
# 1 Above the National average 3.69 887
# 2 Same as the National average 3.11 996
# 3 NA 2.86 99
# 4 Below the National average 2.42 1079
graph_by_experience <- avg_by_experience %>%
ggplot(aes(x = Patient.experience.national.comparison, y = avg_rating, fill = Patient.experience.national.comparison,
label = paste("(", round(avg_rating, 2), ",", hospital_count, ")"), vjust = -2)) +
geom_bar(stat = "identity") +
geom_text(size = 3, vjust = -0.5) +
theme_classic() +
xlab("Hospitals by National Patient Experience Comparison") +
ylab("Average rating") +
ggtitle("Average Ratings by Experience")
graph_by_experience
experience_gp <- round(avg_by_experience$avg_rating[1] - avg_by_experience$avg_rating[4], 2)# Average medical ratings
avg_by_medical <- hospital_ratings %>%
group_by(Efficient.use.of.medical.imaging.national.comparison) %>%
summarise(avg_rating = mean(Hospital.overall.rating, na.rm = T), hospital_count = n()) %>%
arrange(desc(avg_rating))
avg_by_medical## # A tibble: 4 x 3
## Efficient.use.of.medical.imaging.national.comp… avg_rating hospital_count
## <chr> <dbl> <int>
## 1 <NA> 3.09 556
## 2 Same as the National average 3.04 1806
## 3 Above the National average 3.03 359
## 4 Below the National average 2.86 340# # A tibble: 4 x 3
# Efficient.use.of.medical.imaging.national.comparison avg_rating hospital_count
# <chr> <dbl> <int>
# 1 NA 3.09 556
# 2 Same as the National average 3.04 1806
# 3 Above the National average 3.03 359
# 4 Below the National average 2.86 340
# there's not much difference between the average ratings of above and same as
# national average hospitals by medical group variables
graph_by_medical <- avg_by_medical %>%
ggplot(aes(x = Efficient.use.of.medical.imaging.national.comparison, y = avg_rating, fill = Efficient.use.of.medical.imaging.national.comparison,
label = paste("(", round(avg_rating, 2), ",", hospital_count, ")"), vjust = -2)) +
geom_bar(stat = "identity") +
geom_text(size = 2, vjust = -0.5) +
theme_classic() +
xlab("Hospitals by National Use of Medical Imaging Comparison") +
ylab("Average rating") +
ggtitle("Average Ratings by Medical")
graph_by_medical
medical_gp <- round(avg_by_medical$avg_rating[3] - avg_by_medical$avg_rating[4], 2)
# Average timeliness ratings
avg_by_timeliness <- hospital_ratings %>%
group_by(Timeliness.of.care.national.comparison) %>%
summarise(avg_rating = mean(Hospital.overall.rating, na.rm = T), hospital_count = n()) %>%
arrange(desc(avg_rating))
avg_by_timeliness## # A tibble: 4 x 3
## Timeliness.of.care.national.comparison avg_rating hospital_count
## <chr> <dbl> <int>
## 1 <NA> 3.62 151
## 2 Above the National average 3.22 826
## 3 Same as the National average 3.12 1185
## 4 Below the National average 2.62 899# # A tibble: 4 x 3
# Timeliness.of.care.national.comparison avg_rating hospital_count
# <chr> <dbl> <int>
# 1 NA 3.62 151
# 2 Above the National average 3.22 826
# 3 Same as the National average 3.12 1185
# 4 Below the National average 2.62 899
# timeliness has a significant effect on the average rating (from 2.62 to 3.22)
graph_by_timeliness <- avg_by_timeliness %>%
ggplot(aes(x = Timeliness.of.care.national.comparison, y = avg_rating, fill = Timeliness.of.care.national.comparison,
label = paste("(", round(avg_rating, 2), ",", hospital_count, ")"), vjust = -2)) +
geom_bar(stat = "identity") +
geom_text(size = 3, vjust = -0.5) +
theme_classic() +
xlab("Hospitals by National Timeliness of care Comparison") +
ylab("Average rating") +
ggtitle("Average Ratings by Timeliness")
graph_by_timeliness
timeliness_gp <- round(avg_by_timeliness$avg_rating[2] - avg_by_timeliness$avg_rating[4], 2)
# Average effectiveness ratings
avg_by_effectiveness <- hospital_ratings %>%
group_by(Effectiveness.of.care.national.comparison) %>%
summarise(avg_rating = mean(Hospital.overall.rating, na.rm = T), hospital_count = n()) %>%
arrange(desc(avg_rating))
avg_by_effectiveness## # A tibble: 3 x 3
## Effectiveness.of.care.national.comparison avg_rating hospital_count
## <chr> <dbl> <int>
## 1 Above the National average 3.08 991
## 2 Same as the National average 3.07 1665
## 3 Below the National average 2.72 405# # A tibble: 4 x 3
# Effectiveness.of.care.national.comparison avg_rating hospital_count
# <chr> <dbl> <int>
# 1 Above the National average 3.08 991
# 2 Same as the National average 3.07 1665
# 3 Below the National average 2.72 405
graph_by_effectiveness <- avg_by_effectiveness %>%
ggplot(aes(x = Effectiveness.of.care.national.comparison, y = avg_rating, fill = Effectiveness.of.care.national.comparison,
label = paste("(", round(avg_rating, 2), ",", hospital_count, ")"), vjust = -2)) +
geom_bar(stat = "identity") +
geom_text(size = 3, vjust = -0.5) +
theme_classic() +
xlab("Hospitals by National Effectiveness of care Comparison") +
ylab("Average rating") +
ggtitle("Average Ratings by Effectiveness")
graph_by_effectiveness
effectiveness_gp <- round(avg_by_effectiveness$avg_rating[1] - avg_by_effectiveness$avg_rating[3], 2)
# Average Star ratings by each group and its impact over the ratings
group_impact <- data.frame(readmission = readmission_gp, mortality = mortality_gp, safety = safety_gp, experience = experience_gp,
medical = medical_gp, timeliness = timeliness_gp, effectiveness = effectiveness_gp)
group_impacts_in_order <- t(group_impact[order(group_impact, decreasing = T)])
group_impacts_in_order## [,1]
## experience 1.27
## readmission 1.19
## safety 1.08
## mortality 0.81
## timeliness 0.60
## effectiveness 0.36
## medical 0.17# [,1]
# experience 1.27
# readmission 1.19
# safety 1.08
# mortality 0.81
# timeliness 0.60
# effectiveness 0.36
# medical 0.17
# As per the cms measures the top 4 experience, readmission, safety and mortality have a 22% of weightage
# and the last 3 groups timeliness, effectiveness, medical have 4% of weightage
grid_plot1 <- plot_grid(graph_by_readmission, graph_by_mortality, graph_by_safety, graph_by_experience, graph_by_medical, graph_by_timeliness, graph_by_effectiveness)
grid_plot1
Bivariate analysis
# correlation of readmission
readmission <- read_master
str(readmission)## 'data.frame': 4818 obs. of 16 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ Hospital.Name : chr "SOUTHEAST ALABAMA MEDICAL CENTER" "MARSHALL MEDICAL CENTER SOUTH" "ELIZA COFFEE MEMORIAL HOSPITAL" "MIZELL MEMORIAL HOSPITAL" ...
## $ Address : chr "1108 ROSS CLARK CIRCLE" "2505 U S HIGHWAY 431 NORTH" "205 MARENGO STREET" "702 N MAIN ST" ...
## $ City : chr "DOTHAN" "BOAZ" "FLORENCE" "OPP" ...
## $ State : chr "AL" "AL" "AL" "AL" ...
## $ ZIP.Code : int 36301 35957 35631 36467 36049 35235 35968 35007 35233 35660 ...
## $ County.Name : chr "HOUSTON" "MARSHALL" "LAUDERDALE" "COVINGTON" ...
## $ Phone.Number : num 3.35e+09 2.57e+09 2.57e+09 3.34e+09 3.34e+09 ...
## $ READM_30_AMI_score : num 0.409 0.2 0.826 NA NA ...
## $ READM_30_CABG_score : num -0.615 NA -0.704 NA NA ...
## $ READM_30_COPD_score : num -0.8668 1.5743 0.1569 0.0782 0.6294 ...
## $ READM_30_HF_score : num 0.3704 0.0364 0.9047 0.5707 -0.765 ...
## $ READM_30_HIP_KNEE_score : num -0.884 -1.968 -0.703 NA NA ...
## $ READM_30_HOSP_WIDE_score: num 0.215 0.821 0.215 -1.237 -0.148 ...
## $ READM_30_PN_score : num -1.105 0.496 -0.549 -0.131 0.774 ...
## $ READM_30_STK_score : num -0.125 -0.777 0.528 -0.125 NA ...correlation_readmission <- round(cor(readmission[, -c(1:8)], use = "pairwise.complete.obs"), 2)
col1 <- colorRampPalette(c("#7F0000", "red", "#FF7F00", "yellow", "white", "cyan", "#007FFF", "blue", "#00007F"))
col2 <- colorRampPalette(c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7", "#FFFFFF", "#D1E5F0", "#92C5DE", "#4393C3", "#2166AC", "#053061"))
col3 <- colorRampPalette(c("red", "white", "blue"))
col4 <- colorRampPalette(c("#7F0000", "red", "#FF7F00", "yellow", "#7FFF7F", "cyan", "#007FFF", "blue", "#00007F"))
corrplot(correlation_readmission, method = "shade", type = "full", col = col1(20), addCoef.col = "maroon")
write.csv(correlation_readmission, "correlation_readmission.csv")
# Visual Observations:
# The correlation measures for READM_30_HIP_KNEE_score is very low accross all the variables.
# The correlation measures for READM_30_CABG_score is very low accross all the variables.
# READM_30_HOSP_WIDE_score has very good correlation measures accross all the variables.
# Now let us check, how the measures have an impact on the ratings.
readmission <- readmission[, -c(2:8)]
readmission_rating <- merge(readmission, master_data_y, by = "Provider.ID")
readmission_group_summary <- readmission_rating[, -1] %>%
group_by(Hospital.overall.rating) %>%
summarise_all(funs(mean(., na.rm = T)))## Warning: funs() is soft deprecated as of dplyr 0.8.0
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once per session.readmission_group_summary## # A tibble: 6 x 9
## Hospital.overal… READM_30_AMI_sc… READM_30_CABG_s… READM_30_COPD_s…
## <int> <dbl> <dbl> <dbl>
## 1 1 -0.588 -0.579 -1.01
## 2 2 -0.413 -0.214 -0.392
## 3 3 0.0501 -0.0370 0.0135
## 4 4 0.362 0.313 0.346
## 5 5 1.01 0.454 0.965
## 6 NA -0.698 0.228 -0.0357
## # … with 5 more variables: READM_30_HF_score <dbl>,
## # READM_30_HIP_KNEE_score <dbl>, READM_30_HOSP_WIDE_score <dbl>,
## # READM_30_PN_score <dbl>, READM_30_STK_score <dbl># # A tibble: 6 x 9
# Hospital.overall.rating READM_30_AMI_score READM_30_CABG_score READM_30_COPD_score READM_30_HF_score READM_30_HIP_KNEE_score READM_30_HOSP_WIDE_score READM_30_PN_score READM_30_STK_score
# <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 -0.588 -0.579 -1.01 -1.14 -0.309 -1.75 -1.24 -0.946
# 2 -0.413 -0.214 -0.392 -0.480 -0.283 -0.787 -0.609 -0.323
# 3 0.0501 -0.0370 0.0135 -0.00873 -0.0543 -0.0208 0.0322 0.0372
# 4 0.362 0.313 0.346 0.436 0.202 0.612 0.409 0.327
# 5 1.01 0.454 0.965 1.26 0.883 1.66 0.850 0.663
# NA -0.698 0.228 -0.0357 -0.0303 -0.00712 0.00494 0.123 0.155
# We notice that, the ratings increase as all scores of the measures for readmission increase.
# Hence, all the readmission measures have an important role in predicting the ratings.
# correlation of mortality
mortality <- mort_master
str(mortality)## 'data.frame': 4818 obs. of 15 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ Hospital.Name : chr "SOUTHEAST ALABAMA MEDICAL CENTER" "MARSHALL MEDICAL CENTER SOUTH" "ELIZA COFFEE MEMORIAL HOSPITAL" "MIZELL MEMORIAL HOSPITAL" ...
## $ Address : chr "1108 ROSS CLARK CIRCLE" "2505 U S HIGHWAY 431 NORTH" "205 MARENGO STREET" "702 N MAIN ST" ...
## $ City : chr "DOTHAN" "BOAZ" "FLORENCE" "OPP" ...
## $ State : chr "AL" "AL" "AL" "AL" ...
## $ ZIP.Code : int 36301 35957 35631 36467 36049 35235 35968 35007 35233 35660 ...
## $ County.Name : chr "HOUSTON" "MARSHALL" "LAUDERDALE" "COVINGTON" ...
## $ Phone.Number : num 3.35e+09 2.57e+09 2.57e+09 3.34e+09 3.34e+09 ...
## $ MORT_30_AMI_score : num 1.25 -1.55 -2.1 NA NA ...
## $ MORT_30_CABG_score : num -0.996 NA -0.881 NA NA ...
## $ MORT_30_COPD_score : num -1.094 0.434 0.883 -1.094 -0.105 ...
## $ MORT_30_HF_score : num -0.166 -2.286 -2.355 -1.534 -0.371 ...
## $ MORT_30_PN_score : num 0.428 -2.103 -0.862 -1.148 0.332 ...
## $ MORT_30_STK_score : num -0.282 -0.342 -1.784 -1.003 NA ...
## $ PSI_4_SURG_COMP_score: num -1.71 -2.3 -3.33 NA NA ...correlation_mortality <- round(cor(mortality[, -c(1:8)], use = "pairwise.complete.obs"), 2)
corrplot(correlation_mortality, method = "shade", type = "full", col = col1(20), addCoef.col = "maroon")
write.csv(correlation_mortality, "correlation_mortality.csv")
# The correlation values are too low compared to the correlation values for readmission.
# With low correlation values, we see HF, PN and STK scores have some significance.
# Probably these measures would have an impact in the ratings.
# Now let us check, how the measures have an impact on the ratings.
mortality <- mortality[, -c(2:8)]
mortality_rating <- merge(mortality, master_data_y, by = "Provider.ID")
mortality_group_summary <- mortality_rating[, -1] %>%
group_by(Hospital.overall.rating) %>%
summarise_all(funs(mean(., na.rm = T)))
mortality_group_summary## # A tibble: 6 x 8
## Hospital.overal… MORT_30_AMI_sco… MORT_30_CABG_sc… MORT_30_COPD_sc…
## <int> <dbl> <dbl> <dbl>
## 1 1 -0.436 -0.226 -0.411
## 2 2 -0.230 -0.118 -0.199
## 3 3 0.00607 -0.0151 -0.0153
## 4 4 0.235 0.133 0.180
## 5 5 0.809 0.394 0.597
## 6 NA -0.234 -0.136 0.0583
## # … with 4 more variables: MORT_30_HF_score <dbl>, MORT_30_PN_score <dbl>,
## # MORT_30_STK_score <dbl>, PSI_4_SURG_COMP_score <dbl># # A tibble: 6 x 8
# Hospital.overall.rating MORT_30_AMI_score MORT_30_CABG_score MORT_30_COPD_score MORT_30_HF_score MORT_30_PN_score MORT_30_STK_score PSI_4_SURG_COMP_score
# <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 -0.436 -0.226 -0.411 0.0892 -0.503 -0.554 -0.601
# 2 -0.230 -0.118 -0.199 -0.104 -0.339 -0.0885 -0.240
# 3 0.00607 -0.0151 -0.0153 -0.0783 -0.0513 -0.0146 0.0177
# 4 0.235 0.133 0.180 0.210 0.376 0.168 0.290
# 5 0.809 0.394 0.597 0.753 0.899 0.395 0.443
# NA -0.234 -0.136 0.0583 -0.251 -0.0476 -0.132 1.40
# We Can see that the rating increases as each measure score increases
# but for HF score, it behaves differently.
# AMI, HF, ON have an effect on the ratings.# correlation of safety
safety <- safe_master
str(safety)## 'data.frame': 4818 obs. of 16 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ Hospital.Name : chr "SOUTHEAST ALABAMA MEDICAL CENTER" "MARSHALL MEDICAL CENTER SOUTH" "ELIZA COFFEE MEMORIAL HOSPITAL" "MIZELL MEMORIAL HOSPITAL" ...
## $ Address : chr "1108 ROSS CLARK CIRCLE" "2505 U S HIGHWAY 431 NORTH" "205 MARENGO STREET" "702 N MAIN ST" ...
## $ City : chr "DOTHAN" "BOAZ" "FLORENCE" "OPP" ...
## $ State : chr "AL" "AL" "AL" "AL" ...
## $ ZIP.Code : int 36301 35957 35631 36467 36049 35235 35968 35007 35233 35660 ...
## $ County.Name : chr "HOUSTON" "MARSHALL" "LAUDERDALE" "COVINGTON" ...
## $ Phone.Number : num 3.35e+09 2.57e+09 2.57e+09 3.34e+09 3.34e+09 ...
## $ COMP_HIP_KNEE_score: num -1.355 0.0745 -1.355 NA NA ...
## $ HAI_1_SIR_score : num -2.351 -1.023 0.389 NA NA ...
## $ HAI_2_SIR_score : num -2.088 0.05 -0.357 1.054 NA ...
## $ HAI_3_SIR_score : num -1.135 0.723 0.818 NA NA ...
## $ HAI_4_SIR_score : num 1.02 NA NA NA NA ...
## $ HAI_5_SIR_score : num 0.647 -0.457 -0.312 NA NA ...
## $ HAI_6_SIR_score : num 0.0566 0.7984 0.5887 1.5849 0.4489 ...
## $ PSI_90_SAFETY_score: num 1.2108 0.2304 -0.1156 0.5764 -0.0579 ...correlation_safety <- round(cor(safety[, -c(1:8)], use = "pairwise.complete.obs"), 2)
corrplot(correlation_safety, method = "shade", type = "full", col = col1(20), addCoef.col = "maroon")
write.csv(correlation_safety, "correlation_safety.csv")
# COMP_HIP_KNEE_score has a slight negative correlation between HAI_1,2,3 and slight +ve correlation for HAI_4,5,6 and PSI.
# HAI-1, 2 and 3 are correlated compared to the other measures, all are not correlated with HAI-5, HAI-6, PSI and COMP-HIP-KNEE score
# HAI-1 has correlation with HAI-5.
# Now let us check, how the measures have an impact on the ratings.
safety <- safety[, -(2:8)]
safety_rating <- merge(safety, master_data_y, by = "Provider.ID")
safety_group_summary <- safety_rating[, -1] %>%
group_by(Hospital.overall.rating) %>%
summarise_all(funs(mean(., na.rm = T)))
safety_group_summary## # A tibble: 6 x 9
## Hospital.overal… COMP_HIP_KNEE_s… HAI_1_SIR_score HAI_2_SIR_score
## <int> <dbl> <dbl> <dbl>
## 1 1 -0.204 -0.445 -0.526
## 2 2 -0.149 -0.172 -0.109
## 3 3 -0.0765 0.0489 0.0654
## 4 4 0.125 0.177 0.0745
## 5 5 0.846 0.232 0.120
## 6 NA 0.153 -0.0970 -0.0262
## # … with 5 more variables: HAI_3_SIR_score <dbl>, HAI_4_SIR_score <dbl>,
## # HAI_5_SIR_score <dbl>, HAI_6_SIR_score <dbl>,
## # PSI_90_SAFETY_score <dbl># # A tibble: 6 x 9
# Hospital.overall.rating COMP_HIP_KNEE_score HAI_1_SIR_score HAI_2_SIR_score HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score HAI_6_SIR_score PSI_90_SAFETY_score
# <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 -0.204 -0.445 -0.526 -0.173 -0.395 -0.341 -0.0257 -1.88
# 2 -0.149 -0.172 -0.109 -0.0459 0.0362 -0.0710 -0.100 -0.531
# 3 -0.0765 0.0489 0.0654 0.0119 0.0434 0.00232 -0.0240 0.0652
# 4 0.125 0.177 0.0745 0.0613 -0.0488 0.152 0.00476 0.471
# 5 0.846 0.232 0.120 -0.0285 0.166 0.280 0.510 0.942
# NA 0.153 -0.0970 -0.0262 0.0660 0.0439 -0.0714 0.403 0.0689
# As per the above summary, it looks like COMP_HIP_KNEE_score, PSI_90_SAFETY_score have a significant impact on the ratings.
# correlation of experience
experience <- expe_master
correlation_experience <- round(cor(experience[, -c(1:8)], use = "pairwise.complete.obs"), 2)
par(pin = c(5, 5)) ## (width, height) in inches
par(omi = c(0, 0.5, 0.5, 0.5)) ## (bottom, left, top, right) in inches
corrplot(correlation_experience, method = "shade", type = "full", col = col1(20), addCoef.col = "maroon", tl.cex = 0.7, tl.offset = 0.5, number.cex = 0.8, tl.pos = 't')
write.csv(correlation_experience, "correlation_experience.csv")
# we can visualize that all the variables are positively correlated with each other.
# This implies that the overall hospital experience, which is all about the hospitality and clear communication, helps improve the ratings.
# All the measures play a vital role in the ratings.
# Now let us check, how the measures have an impact on the ratings.
experience <- experience[, -c(2:8)]
experience_rating <- merge(experience, master_data_y, by = "Provider.ID")
experience_group_summary <- experience_rating[, -1] %>%
group_by(Hospital.overall.rating) %>%
summarise_all(funs(mean(., na.rm = T)))
experience_group_summary## # A tibble: 6 x 12
## Hospital.overal… H_CLEAN_LINEAR_… H_COMP_1_LINEAR… H_COMP_2_LINEAR…
## <int> <dbl> <dbl> <dbl>
## 1 1 -1.28 -1.57 -1.17
## 2 2 -0.705 -0.790 -0.709
## 3 3 -0.0223 -0.0303 -0.00778
## 4 4 0.530 0.602 0.504
## 5 5 0.922 1.14 0.822
## 6 NA 0.378 0.473 0.410
## # … with 8 more variables: H_COMP_3_LINEAR_SCORE <dbl>,
## # H_COMP_4_LINEAR_SCORE <dbl>, H_COMP_5_LINEAR_SCORE <dbl>,
## # H_COMP_6_LINEAR_SCORE <dbl>, H_COMP_7_LINEAR_SCORE <dbl>,
## # H_HSP_RATING_LINEAR_SCORE <dbl>, H_QUIET_LINEAR_SCORE <dbl>,
## # H_RECMND_LINEAR_SCORE <dbl># # A tibble: 6 x 12
# Hospital.overall.rating H_CLEAN_LINEAR_~ H_COMP_1_LINEAR~ H_COMP_2_LINEAR~ H_COMP_3_LINEAR~ H_COMP_4_LINEAR~ H_COMP_5_LINEAR~ H_COMP_6_LINEAR~ H_COMP_7_LINEAR~ H_HSP_RATING_LI~ H_QUIET_LINEAR_~ H_RECMND_LINEAR~
# <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 -1.28 -1.57 -1.17 -1.64 -1.48 -1.31 -1.32 -1.37 -1.53 -1.09 -1.31
# 2 -0.705 -0.790 -0.709 -0.806 -0.767 -0.739 -0.661 -0.784 -0.790 -0.578 -0.737
# 3 -0.0223 -0.0303 -0.00778 -0.00465 -0.0252 -0.0574 -0.0351 -0.0777 -0.0649 -0.0224 -0.0874
# 4 0.530 0.602 0.504 0.566 0.577 0.577 0.524 0.620 0.617 0.364 0.600
# 5 0.922 1.14 0.822 1.06 1.05 1.02 0.883 1.25 1.38 0.958 1.37
# NA 0.378 0.473 0.410 0.613 0.449 0.570 0.425 0.605 0.554 0.729 0.500
# This implies all the measures in experience are highly important measures.# Correlation of medical
medical <- medi_master
correlation_medical <- round(cor(medical[, -c(1:8)], use = "pairwise.complete.obs"), 2)
corrplot(correlation_medical, method = "shade", type = "full", col = col1(20), addCoef.col = "maroon")
write.csv(correlation_medical, "correlation_medical.csv")
# Only OP-10 and OP_11 have some good correlation.
# Rest all the correlations are negligible.
# Now let us check, how the measures have an impact on the ratings.
medical <- medical[, -c(2:8)]
medical_rating <- merge(medical, master_data_y, by = "Provider.ID")
medical_group_summary <- medical_rating[, -1] %>%
group_by(Hospital.overall.rating) %>%
summarise_all(funs(mean(., na.rm = T)))
medical_group_summary## # A tibble: 6 x 6
## Hospital.overal… OP_10_score OP_11_score OP_13_score OP_14_score
## <int> <dbl> <dbl> <dbl> <dbl>
## 1 1 0.0750 0.132 0.0318 -0.685
## 2 2 -0.0888 -0.0955 -0.0360 -0.273
## 3 3 -0.0114 -0.0134 0.0318 0.0235
## 4 4 0.133 0.110 -0.0156 0.100
## 5 5 0.131 0.240 -0.135 0.317
## 6 NA -0.213 -0.272 0.00673 1.06
## # … with 1 more variable: OP_8_score <dbl># # A tibble: 6 x 6
# Hospital.overall.rating OP_10_score OP_11_score OP_13_score OP_14_score OP_8_score
# <int> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 0.0750 0.132 0.0318 -0.685 0.130
# 2 -0.0888 -0.0955 -0.0360 -0.273 0.0312
# 3 -0.0114 -0.0134 0.0318 0.0235 -0.0176
# 4 0.133 0.110 -0.0156 0.100 0.00368
# 5 0.131 0.240 -0.135 0.317 0.0970
# NA -0.213 -0.272 0.00673 1.06 -0.612
# Only OP_14 seems significant, but the overall weight of medical group itself is quite less
# Correlation of timeliness
timeliness <- time_master
correlation_timeliness <- round(cor(timeliness[, -c(1:8)], use = "pairwise.complete.obs"), 2)
corrplot(correlation_timeliness, method = "shade", type = "full", col = col1(20), addCoef.col = "maroon")
write.csv(correlation_timeliness, "correlation_timeliness.csv")
# All ED scores have good correlation with OP_18b, OP_20, and OP_21.
# Even OP_18b, 20 and 21 have good correlations with each other.
# Now let us check, how the measures have an impact on the ratings.
timeliness <- timeliness[, -c(2:8)]
timeliness_rating <- merge(timeliness, master_data_y, by = "Provider.ID")
timeliness_group_summary <- timeliness_rating[, -1] %>%
group_by(Hospital.overall.rating) %>%
summarise_all(funs(mean(., na.rm = T)))
timeliness_group_summary## # A tibble: 6 x 8
## Hospital.overal… ED_1b_score ED_2b_score OP_18b_score OP_20_score
## <int> <dbl> <dbl> <dbl> <dbl>
## 1 1 -1.40 -1.30 -0.982 -1.01
## 2 2 -0.514 -0.508 -0.432 -0.266
## 3 3 0.101 0.0991 0.0551 0.0390
## 4 4 0.242 0.206 0.0933 0.152
## 5 5 0.301 0.174 -0.0454 0.125
## 6 NA 0.393 0.499 0.747 0.288
## # … with 3 more variables: OP_21_score <dbl>, OP_3b_score <dbl>,
## # OP_5_score <dbl># # A tibble: 6 x 8
# Hospital.overall.rating ED_1b_score ED_2b_score OP_18b_score OP_20_score OP_21_score OP_3b_score OP_5_score
# <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 -1.40 -1.30 -0.982 -1.01 -0.763 -0.403 -0.841
# 2 -0.514 -0.508 -0.432 -0.266 -0.294 -0.137 -0.0914
# 3 0.101 0.0991 0.0551 0.0390 0.0297 -0.0210 -0.0155
# 4 0.242 0.206 0.0933 0.152 0.236 0.182 0.204
# 5 0.301 0.174 -0.0454 0.125 0.263 0.542 0.341
# NA 0.393 0.499 0.747 0.288 0.160 -0.355 -0.181
# The above implies ED_1b and OP_21, OP_3b, OP_5 are the most important measures
# Since the overall weightage of the group is only 4%, the impact might be less.
# Correlation of effectiveness
effectiveness <- effe_master
correlation_effectiveness <- round(cor(effectiveness[, -c(1:8)], use = "pairwise.complete.obs"), 2)
par(pin = c(75, 75)) ## (width, height) in inches
par(omi = c(0, 0.8, 0.8, 0.8)) ## (bottom, left, top, right) in inches
corrplot(correlation_effectiveness, method = "shade", type = "full", col = col1(20), addCoef.col = "maroon", tl.cex = 0.8, tl.offset = 0.7, number.cex = 0.7,
tl.pos = 'b')
write.csv(correlation_effectiveness, "correlation_effectiveness.csv")
# We can see very good correlations for STK_4, STK5,STK_6, STK_8, VTE_1, VTE_2 and VTE_6.
# CAC_3 and IMM_2, STK_6, STK_8, VTE_1 are positively correlated.
# Now let us check, how the measures have an impact on the ratings.
effectiveness <- effectiveness[, -c(2:8)]
effectiveness_rating <- merge(effectiveness, master_data_y, by = "Provider.ID")
effectiveness_group_summary <- effectiveness_rating[, -1] %>%
group_by(Hospital.overall.rating) %>%
summarise_all(funs(mean(., na.rm = T)))
effectiveness_group_summary## # A tibble: 6 x 19
## Hospital.overal… CAC_3_score IMM_2_score IMM_3_OP_27_FAC… OP_22_score
## <int> <dbl> <dbl> <dbl> <dbl>
## 1 1 0.0933 -0.237 -0.286 -0.669
## 2 2 -0.0145 0.0486 -0.217 -0.237
## 3 3 0.215 0.0779 0.00515 0.000349
## 4 4 -0.0927 0.229 0.205 0.207
## 5 5 0.397 0.311 0.227 0.401
## 6 NA -0.254 -0.980 -0.0751 0.268
## # … with 14 more variables: OP_23_score <dbl>, OP_29_score <dbl>,
## # OP_30_score <dbl>, OP_4_score <dbl>, PC_01_score <dbl>,
## # STK_4_score <dbl>, STK_5_score <dbl>, STK_6_score <dbl>,
## # STK_8_score <dbl>, VTE_1_score <dbl>, VTE_2_score <dbl>,
## # VTE_3_score <dbl>, VTE_5_score <dbl>, VTE_6_score <dbl>write.csv(effectiveness_group_summary, "effectiveness_rating_summary.csv")
# We see a mixed behaviour here, ratings increase as measures increase for few and viceversa.
# This implies that VTE (1, 2, 3, 5) measures are correlated to each other, they might play role in the ratings.
# Similar observations are there for STK measuresEDA Completed
Random Forest Modelling
library(caTools)
library(rpart)
library(randomForest)## randomForest 4.6-14## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:gridExtra':
##
## combine## The following object is masked from 'package:ggplot2':
##
## margin## The following object is masked from 'package:dplyr':
##
## combinelibrary(rpart.plot)
library(RColorBrewer)
library(rattle)## Rattle: A free graphical interface for data science with R.
## Version 5.3.0 Copyright (c) 2006-2018 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.##
## Attaching package: 'rattle'## The following object is masked from 'package:randomForest':
##
## importancelibrary(caret)## Loading required package: lattice# We will use the cleaned master data as data set-
# cleaned_master_data
### split the train and test set
set.seed(123)
data = sample.split(cleaned_master_data, SplitRatio = 0.3)
train = subset(cleaned_master_data, data == FALSE)
test = subset(cleaned_master_data, data == TRUE)
dim(train)## [1] 2570 54# [1] 2570 54
dim(test)## [1] 1078 54# [1] 1078 54
str(train)## 'data.frame': 2570 obs. of 54 variables:
## $ Provider.ID : int 10005 10006 10007 10008 10012 10016 10019 10021 10022 10023 ...
## $ READM_30_AMI_score : num 0.1999 0.8261 0.0956 0.0956 0.8261 ...
## $ READM_30_COPD_score : num 1.5743 0.1569 0.0782 0.6294 0.6294 ...
## $ READM_30_HF_score : num 0.0364 0.9047 0.5707 -0.765 -0.9653 ...
## $ READM_30_HIP_KNEE_score : num -1.9681 -0.7033 0.0194 0.0194 0.0194 ...
## $ READM_30_HOSP_WIDE_score : num 0.821 0.215 -1.237 -0.148 0.336 ...
## $ READM_30_PN_score : num 0.496 -0.549 -0.131 0.774 -1.593 ...
## $ READM_30_STK_score : num -0.777 0.5279 -0.1245 0.0619 -0.2177 ...
## $ MORT_30_AMI_score : num -1.5457 -2.1047 0.0514 0.0514 -2.1846 ...
## $ MORT_30_COPD_score : num 0.434 0.883 -1.094 -0.105 -1.004 ...
## $ MORT_30_HF_score : num -2.286 -2.355 -1.534 -0.371 -0.234 ...
## $ MORT_30_PN_score : num -2.103 -0.862 -1.148 0.332 -3.772 ...
## $ MORT_30_STK_score : num -0.3421 -1.7844 -1.0031 0.0786 -0.5224 ...
## $ COMP_HIP_KNEE_score : num 0.0745 -1.355 0.0745 0.0745 0.4318 ...
## $ HAI_1_SIR_score : num -1.023 0.389 0.149 0.149 0.149 ...
## $ HAI_2_SIR_score : num 0.05 -0.357 1.054 0.127 1.054 ...
## $ HAI_3_SIR_score : num 0.723 0.818 0.16 0.16 0.16 ...
## $ HAI_6_SIR_score : num 0.798 0.589 1.585 0.449 1.148 ...
## $ PSI_90_SAFETY_score : num 0.2304 -0.1156 0.5764 -0.0579 -0.0579 ...
## $ H_CLEAN_LINEAR_SCORE : num -1.1116 -1.1116 0.4419 -0.0759 -0.3349 ...
## $ H_COMP_1_LINEAR_SCORE : num -0.129 -0.129 -0.129 0.265 0.265 ...
## $ H_COMP_2_LINEAR_SCORE : num 0.8596 0.8596 1.678 0.0412 0.8596 ...
## $ H_COMP_3_LINEAR_SCORE : num -0.29 -0.5175 0.3923 -0.0626 -0.0626 ...
## $ H_COMP_4_LINEAR_SCORE : num 0.167 -0.22 0.553 0.167 0.167 ...
## $ H_COMP_5_LINEAR_SCORE : num 0.2845 -0.182 0.751 0.0513 -0.4152 ...
## $ H_COMP_6_LINEAR_SCORE : num 0.3087 -1.0964 -0.2534 0.0276 0.3087 ...
## $ H_COMP_7_LINEAR_SCORE : num -0.183 -0.532 0.165 0.165 -0.88 ...
## $ H_HSP_RATING_LINEAR_SCORE : num 0.3915 -1.1451 -0.5304 0.0842 0.0842 ...
## $ H_QUIET_LINEAR_SCORE : num 0.57722 0.57722 1.75119 -0.00976 0.57722 ...
## $ H_RECMND_LINEAR_SCORE : num 0.2213 -0.9226 -0.465 -0.0075 -0.6938 ...
## $ OP_10_score : num -0.423 -0.277 -1.498 0.524 0.28 ...
## $ OP_11_score : num -1.204 -0.245 -0.502 0.389 -1.306 ...
## $ OP_13_score : num -0.3052 2.3296 0.0428 0.0428 0.7885 ...
## $ OP_14_score : num -0.649 -0.97 0.1 1.171 0.1 ...
## $ ED_1b_score : num 0.342 0.594 0.574 0.953 0.865 ...
## $ ED_2b_score : num 0.463 0.357 0.508 0.69 0.751 ...
## $ OP_18b_score : num 0.616 0.235 0.568 1.068 0.544 ...
## $ OP_20_score : num -0.049 1.009 -0.733 -0.049 -0.485 ...
## $ OP_21_score : num -0.38 -0.267 -2.078 0.243 -0.323 ...
## $ OP_5_score : num -0.714 0.248 0.248 0.248 1.595 ...
## $ IMM_2_score : num 0.53 0.613 0.53 0.197 0.613 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num -0.099 0.169 -2.11 -2.781 0.37 ...
## $ OP_22_score : num -0.108 0.438 0.438 0.438 -0.108 ...
## $ OP_29_score : num 0.692 -0.102 -2.627 0.836 0.981 ...
## $ OP_30_score : num 0.5 0.329 -3.448 0.715 0.844 ...
## $ OP_4_score : num 0.546 0.368 -1.232 0.368 0.724 ...
## $ PC_01_score : num 0.324 0.539 0.324 0.324 -2.254 ...
## $ STK_6_score : num -0.9154 0.1984 -3.0191 0.3222 -0.0491 ...
## $ STK_8_score : num 0.618 -0.296 0.344 0.344 0.618 ...
## $ VTE_1_score : num 0.178 0.412 0.334 0.489 0.489 ...
## $ VTE_2_score : num -0.45 -1.282 0.381 0.381 0.215 ...
## $ VTE_3_score : num 0.819 -0.887 0.332 0.332 0.21 ...
## $ VTE_5_score : num 0.6352 -0.0759 0.3685 0.3685 0.6352 ...
## $ Hospital.overall.rating : Factor w/ 5 levels "1","2","3","4",..: 3 2 3 3 3 3 2 4 3 3 ...# We will use mtry=20, we will use ntree = 500,1000 and 1500 as per the mentor's suggestions.
model_rf1 <- randomForest(Hospital.overall.rating ~ ., data = train, promiximity = FALSE, ntree = 500, mtry = 20, do.trace = TRUE, na.action = na.omit)## ntree OOB 1 2 3 4 5
## 1: 36.66% 60.00% 37.87% 29.20% 42.63% 67.74%
## 2: 36.65% 53.49% 41.90% 29.93% 40.90% 65.12%
## 3: 37.28% 52.46% 41.97% 30.13% 42.20% 69.81%
## 4: 37.34% 44.12% 43.95% 31.50% 40.25% 60.00%
## 5: 37.06% 44.59% 42.92% 30.63% 41.23% 62.50%
## 6: 35.09% 44.87% 42.95% 28.43% 38.83% 52.24%
## 7: 35.09% 45.12% 43.45% 26.82% 41.11% 56.72%
## 8: 34.13% 48.19% 40.34% 26.68% 39.82% 52.86%
## 9: 33.04% 53.01% 40.38% 25.04% 37.86% 56.34%
## 10: 31.47% 44.05% 39.32% 24.14% 35.93% 52.11%
## 11: 31.51% 44.71% 38.30% 23.07% 37.91% 58.90%
## 12: 31.04% 51.76% 37.71% 23.17% 35.62% 57.53%
## 13: 29.46% 47.06% 37.92% 21.08% 33.98% 57.53%
## 14: 29.22% 48.24% 36.65% 21.08% 34.13% 54.79%
## 15: 28.93% 48.24% 34.75% 20.65% 34.58% 60.27%
## 16: 27.89% 49.41% 36.02% 19.43% 32.19% 57.53%
## 17: 27.95% 48.24% 37.92% 19.02% 32.34% 54.05%
## 18: 27.00% 50.59% 36.44% 17.98% 31.59% 52.70%
## 19: 27.39% 55.29% 35.59% 18.53% 32.19% 51.35%
## 20: 26.81% 52.94% 36.65% 17.51% 31.15% 54.05%
## 21: 25.80% 55.29% 34.53% 16.64% 30.40% 51.35%
## 22: 25.68% 54.12% 35.59% 16.01% 30.55% 51.35%
## 23: 25.41% 51.76% 36.23% 16.17% 29.06% 51.35%
## 24: 25.45% 54.12% 36.02% 16.09% 29.36% 50.00%
## 25: 24.59% 52.94% 33.90% 15.54% 28.61% 51.35%
## 26: 25.10% 56.47% 35.38% 15.93% 28.32% 51.35%
## 27: 24.98% 52.94% 35.17% 15.54% 29.36% 50.00%
## 28: 25.25% 54.12% 35.38% 16.09% 28.91% 51.35%
## 29: 25.10% 55.29% 36.02% 15.85% 28.46% 48.65%
## 30: 24.32% 55.29% 34.96% 14.98% 28.02% 47.30%
## 31: 24.24% 54.12% 33.69% 14.91% 28.76% 48.65%
## 32: 24.28% 54.12% 34.11% 14.75% 28.46% 52.70%
## 33: 23.89% 54.12% 33.26% 14.43% 28.17% 52.70%
## 34: 23.93% 54.12% 34.32% 13.80% 28.76% 52.70%
## 35: 24.01% 55.29% 34.96% 13.72% 28.61% 52.70%
## 36: 24.20% 55.29% 34.53% 14.35% 28.46% 52.70%
## 37: 23.54% 54.12% 33.90% 13.25% 28.61% 52.70%
## 38: 23.74% 50.59% 35.17% 13.56% 28.32% 52.70%
## 39: 23.70% 52.94% 34.11% 13.56% 28.61% 52.70%
## 40: 23.35% 49.41% 34.53% 13.01% 28.46% 52.70%
## 41: 23.07% 52.94% 33.69% 12.78% 28.02% 52.70%
## 42: 23.39% 52.94% 33.05% 13.25% 28.76% 52.70%
## 43: 23.77% 57.65% 33.47% 13.49% 28.76% 54.05%
## 44: 23.39% 54.12% 33.47% 12.93% 28.61% 55.41%
## 45: 23.42% 56.47% 33.90% 12.54% 28.91% 55.41%
## 46: 23.74% 56.47% 35.59% 12.93% 28.17% 55.41%
## 47: 23.07% 57.65% 33.47% 12.30% 28.46% 52.70%
## 48: 23.11% 56.47% 33.90% 12.38% 28.32% 52.70%
## 49: 23.39% 60.00% 34.32% 12.46% 28.46% 52.70%
## 50: 23.00% 57.65% 35.38% 11.51% 28.32% 52.70%
## 51: 22.61% 60.00% 34.11% 11.59% 27.57% 50.00%
## 52: 22.53% 60.00% 33.26% 11.44% 28.17% 50.00%
## 53: 22.92% 56.47% 33.47% 12.30% 28.17% 51.35%
## 54: 22.57% 58.82% 33.90% 11.44% 28.02% 50.00%
## 55: 22.45% 57.65% 33.69% 11.59% 27.42% 51.35%
## 56: 22.37% 58.82% 32.42% 11.51% 28.17% 50.00%
## 57: 22.37% 60.00% 32.84% 11.44% 27.87% 50.00%
## 58: 22.37% 60.00% 32.84% 11.12% 28.32% 51.35%
## 59: 22.37% 60.00% 33.05% 11.12% 28.17% 51.35%
## 60: 22.65% 58.82% 33.05% 11.51% 28.61% 51.35%
## 61: 21.95% 58.82% 31.36% 10.88% 28.32% 51.35%
## 62: 22.37% 61.18% 32.42% 11.04% 28.76% 50.00%
## 63: 22.33% 61.18% 32.42% 10.73% 29.21% 50.00%
## 64: 22.57% 61.18% 32.84% 10.88% 29.21% 52.70%
## 65: 22.22% 58.82% 33.26% 10.41% 28.91% 51.35%
## 66: 22.22% 60.00% 33.05% 10.25% 29.06% 52.70%
## 67: 22.45% 60.00% 33.90% 10.65% 28.61% 52.70%
## 68: 22.22% 61.18% 33.69% 10.02% 28.91% 52.70%
## 69: 22.10% 60.00% 33.47% 10.25% 28.46% 51.35%
## 70: 21.98% 58.82% 33.47% 10.17% 28.17% 52.70%
## 71: 22.06% 61.18% 33.69% 10.25% 28.02% 51.35%
## 72: 22.26% 61.18% 33.26% 10.33% 28.91% 51.35%
## 73: 22.41% 63.53% 34.32% 10.25% 28.46% 52.70%
## 74: 22.14% 63.53% 33.69% 10.09% 28.32% 51.35%
## 75: 22.30% 64.71% 32.84% 10.41% 28.76% 51.35%
## 76: 22.02% 62.35% 32.84% 10.17% 28.46% 51.35%
## 77: 21.87% 62.35% 32.42% 9.70% 28.91% 52.70%
## 78: 21.60% 61.18% 33.47% 9.62% 27.27% 54.05%
## 79: 21.56% 61.18% 32.84% 9.31% 28.17% 54.05%
## 80: 21.56% 61.18% 32.42% 9.46% 28.17% 54.05%
## 81: 21.40% 61.18% 32.20% 9.54% 27.57% 54.05%
## 82: 21.67% 62.35% 33.05% 9.78% 27.57% 52.70%
## 83: 21.98% 62.35% 33.47% 9.86% 28.32% 52.70%
## 84: 21.83% 62.35% 33.69% 10.02% 27.27% 52.70%
## 85: 21.48% 61.18% 32.84% 9.86% 27.12% 51.35%
## 86: 21.63% 64.71% 32.63% 9.54% 27.87% 52.70%
## 87: 21.91% 63.53% 33.90% 10.02% 27.27% 52.70%
## 88: 21.52% 63.53% 32.20% 9.70% 27.57% 52.70%
## 89: 21.75% 62.35% 34.32% 9.62% 27.27% 52.70%
## 90: 21.44% 61.18% 33.90% 9.23% 27.27% 52.70%
## 91: 21.44% 62.35% 33.47% 9.31% 27.42% 51.35%
## 92: 21.25% 62.35% 31.57% 9.46% 27.57% 52.70%
## 93: 21.40% 61.18% 32.42% 9.31% 27.87% 54.05%
## 94: 21.05% 62.35% 31.57% 9.23% 27.12% 54.05%
## 95: 21.32% 62.35% 32.20% 9.46% 27.27% 54.05%
## 96: 21.13% 62.35% 31.78% 9.31% 27.27% 52.70%
## 97: 20.97% 60.00% 31.78% 8.83% 27.72% 54.05%
## 98: 21.05% 60.00% 31.57% 9.07% 28.02% 51.35%
## 99: 21.05% 60.00% 31.99% 8.83% 27.87% 54.05%
## 100: 21.25% 62.35% 32.20% 8.99% 28.17% 51.35%
## 101: 21.13% 62.35% 32.20% 8.83% 28.02% 51.35%
## 102: 21.05% 62.35% 32.20% 8.68% 27.87% 52.70%
## 103: 20.93% 60.00% 31.78% 8.75% 27.87% 52.70%
## 104: 20.86% 62.35% 32.20% 8.20% 28.02% 52.70%
## 105: 20.89% 61.18% 32.42% 8.36% 27.87% 52.70%
## 106: 21.32% 61.18% 32.84% 8.99% 28.02% 52.70%
## 107: 21.17% 61.18% 32.42% 8.68% 28.32% 52.70%
## 108: 21.36% 61.18% 33.26% 8.83% 28.17% 52.70%
## 109: 21.44% 60.00% 32.63% 8.91% 28.76% 54.05%
## 110: 21.71% 62.35% 33.26% 9.15% 28.61% 54.05%
## 111: 21.28% 62.35% 32.63% 8.68% 28.32% 54.05%
## 112: 21.44% 62.35% 33.05% 8.68% 28.61% 54.05%
## 113: 21.28% 62.35% 32.84% 8.68% 28.17% 54.05%
## 114: 21.09% 61.18% 32.84% 8.52% 27.87% 54.05%
## 115: 21.48% 61.18% 33.47% 8.60% 28.76% 54.05%
## 116: 20.97% 60.00% 32.84% 8.44% 27.87% 52.70%
## 117: 21.44% 62.35% 33.47% 8.60% 28.61% 52.70%
## 118: 21.28% 63.53% 33.05% 8.36% 28.61% 52.70%
## 119: 21.17% 61.18% 33.47% 8.28% 28.32% 52.70%
## 120: 21.32% 63.53% 33.69% 8.28% 28.46% 52.70%
## 121: 21.13% 63.53% 33.26% 8.36% 27.72% 54.05%
## 122: 21.13% 62.35% 33.26% 8.28% 28.02% 54.05%
## 123: 20.97% 62.35% 31.99% 8.52% 27.87% 54.05%
## 124: 21.28% 62.35% 33.05% 8.60% 28.17% 54.05%
## 125: 21.28% 63.53% 33.47% 8.60% 27.72% 54.05%
## 126: 21.13% 62.35% 32.63% 8.28% 28.46% 54.05%
## 127: 21.32% 64.71% 32.63% 8.60% 28.32% 54.05%
## 128: 21.40% 63.53% 33.05% 8.68% 28.17% 55.41%
## 129: 21.36% 64.71% 33.47% 8.44% 28.17% 54.05%
## 130: 21.36% 64.71% 33.26% 8.52% 28.02% 55.41%
## 131: 21.25% 63.53% 33.47% 8.60% 27.57% 54.05%
## 132: 21.21% 63.53% 33.90% 8.36% 27.57% 54.05%
## 133: 21.05% 63.53% 33.26% 8.44% 27.27% 54.05%
## 134: 21.13% 64.71% 33.69% 8.44% 27.12% 54.05%
## 135: 21.13% 63.53% 33.47% 8.20% 27.87% 54.05%
## 136: 21.05% 63.53% 32.42% 8.44% 27.72% 55.41%
## 137: 20.97% 64.71% 32.84% 8.28% 27.42% 54.05%
## 138: 20.97% 64.71% 32.42% 8.44% 27.42% 54.05%
## 139: 21.28% 64.71% 32.63% 8.83% 27.72% 54.05%
## 140: 21.21% 64.71% 32.42% 8.68% 27.87% 54.05%
## 141: 21.13% 64.71% 31.99% 8.60% 28.02% 54.05%
## 142: 21.13% 65.88% 31.99% 8.60% 27.87% 54.05%
## 143: 21.05% 64.71% 31.99% 8.75% 27.42% 54.05%
## 144: 21.01% 64.71% 31.99% 8.68% 27.42% 54.05%
## 145: 20.89% 64.71% 31.99% 8.60% 27.12% 54.05%
## 146: 20.97% 64.71% 31.99% 8.44% 27.72% 54.05%
## 147: 20.86% 64.71% 31.78% 8.44% 27.42% 54.05%
## 148: 20.78% 63.53% 31.36% 8.60% 27.42% 52.70%
## 149: 20.93% 63.53% 31.99% 8.44% 27.72% 54.05%
## 150: 21.01% 63.53% 31.99% 8.60% 27.72% 54.05%
## 151: 20.86% 62.35% 31.78% 8.44% 27.72% 54.05%
## 152: 20.97% 63.53% 31.99% 8.60% 27.57% 54.05%
## 153: 20.93% 62.35% 31.99% 8.91% 26.83% 55.41%
## 154: 20.86% 63.53% 31.99% 8.60% 26.97% 55.41%
## 155: 20.93% 63.53% 31.78% 8.75% 27.27% 54.05%
## 156: 20.89% 63.53% 31.78% 8.75% 27.12% 54.05%
## 157: 21.01% 63.53% 31.99% 8.99% 26.97% 54.05%
## 158: 21.21% 63.53% 32.84% 8.75% 27.42% 55.41%
## 159: 20.93% 63.53% 31.78% 8.68% 27.27% 55.41%
## 160: 21.09% 63.53% 31.78% 8.91% 27.42% 55.41%
## 161: 20.74% 61.18% 31.36% 8.68% 27.12% 55.41%
## 162: 21.05% 63.53% 31.57% 8.68% 27.87% 55.41%
## 163: 20.97% 62.35% 31.78% 8.36% 28.17% 55.41%
## 164: 20.66% 61.18% 30.93% 8.36% 27.72% 55.41%
## 165: 20.70% 63.53% 30.72% 8.44% 27.57% 55.41%
## 166: 20.93% 64.71% 31.14% 8.36% 28.17% 55.41%
## 167: 20.66% 64.71% 30.72% 8.20% 27.72% 55.41%
## 168: 20.35% 64.71% 30.51% 7.81% 27.42% 55.41%
## 169: 20.62% 64.71% 30.72% 8.36% 27.27% 55.41%
## 170: 20.58% 63.53% 30.72% 8.36% 27.27% 55.41%
## 171: 20.70% 63.53% 31.36% 8.20% 27.57% 55.41%
## 172: 20.31% 64.71% 31.14% 7.89% 26.68% 55.41%
## 173: 20.74% 63.53% 31.36% 8.36% 27.42% 55.41%
## 174: 20.47% 62.35% 31.14% 7.97% 27.42% 55.41%
## 175: 20.54% 61.18% 31.36% 8.20% 27.27% 55.41%
## 176: 20.47% 61.18% 30.93% 8.12% 27.42% 55.41%
## 177: 20.43% 62.35% 30.93% 8.04% 27.27% 55.41%
## 178: 20.54% 62.35% 30.93% 8.12% 27.57% 55.41%
## 179: 20.31% 62.35% 30.93% 7.97% 26.97% 55.41%
## 180: 20.08% 61.18% 30.72% 7.65% 26.97% 55.41%
## 181: 20.31% 62.35% 31.14% 7.89% 26.97% 55.41%
## 182: 20.51% 62.35% 31.36% 7.97% 27.42% 55.41%
## 183: 20.39% 62.35% 31.36% 7.73% 27.42% 55.41%
## 184: 20.47% 63.53% 30.51% 8.04% 27.57% 55.41%
## 185: 20.35% 61.18% 30.72% 8.04% 27.27% 55.41%
## 186: 20.43% 61.18% 30.93% 8.04% 27.42% 55.41%
## 187: 20.47% 61.18% 30.93% 8.12% 27.42% 55.41%
## 188: 20.19% 61.18% 30.72% 7.81% 27.12% 55.41%
## 189: 20.23% 60.00% 30.72% 7.89% 27.27% 55.41%
## 190: 20.16% 60.00% 30.93% 7.89% 26.68% 56.76%
## 191: 20.08% 60.00% 31.14% 7.73% 26.53% 56.76%
## 192: 20.23% 60.00% 30.93% 8.04% 26.68% 56.76%
## 193: 20.31% 60.00% 31.36% 8.12% 26.53% 56.76%
## 194: 20.35% 60.00% 30.93% 8.36% 26.53% 56.76%
## 195: 20.19% 60.00% 31.14% 7.97% 26.53% 56.76%
## 196: 20.27% 61.18% 31.14% 8.12% 26.38% 56.76%
## 197: 20.31% 61.18% 30.93% 8.12% 26.68% 56.76%
## 198: 20.43% 61.18% 31.14% 8.12% 26.97% 56.76%
## 199: 20.47% 60.00% 31.57% 8.12% 26.97% 56.76%
## 200: 20.47% 61.18% 31.78% 8.20% 26.68% 55.41%
## 201: 20.39% 60.00% 31.36% 8.28% 26.68% 55.41%
## 202: 20.39% 60.00% 30.93% 8.12% 27.12% 56.76%
## 203: 20.35% 60.00% 31.14% 8.04% 26.97% 56.76%
## 204: 20.43% 61.18% 31.14% 8.20% 26.83% 56.76%
## 205: 20.35% 61.18% 31.14% 8.12% 26.68% 56.76%
## 206: 20.51% 61.18% 31.57% 8.20% 26.97% 55.41%
## 207: 20.43% 61.18% 31.36% 8.20% 26.83% 55.41%
## 208: 20.35% 61.18% 30.93% 8.12% 26.83% 56.76%
## 209: 20.47% 60.00% 31.57% 8.28% 26.83% 55.41%
## 210: 20.27% 60.00% 31.78% 7.97% 26.53% 55.41%
## 211: 20.51% 60.00% 32.20% 8.12% 26.83% 55.41%
## 212: 20.47% 60.00% 31.57% 8.20% 26.97% 55.41%
## 213: 20.74% 61.18% 32.20% 8.44% 26.97% 55.41%
## 214: 20.43% 61.18% 31.14% 8.44% 26.53% 55.41%
## 215: 20.43% 61.18% 31.36% 8.28% 26.68% 55.41%
## 216: 20.43% 60.00% 30.93% 8.36% 26.97% 55.41%
## 217: 20.31% 60.00% 31.57% 8.20% 26.38% 55.41%
## 218: 20.19% 60.00% 31.14% 8.36% 25.93% 55.41%
## 219: 20.47% 60.00% 31.78% 8.44% 26.38% 55.41%
## 220: 20.39% 60.00% 31.57% 8.12% 26.83% 55.41%
## 221: 20.47% 60.00% 31.99% 8.28% 26.53% 55.41%
## 222: 20.51% 61.18% 31.57% 8.28% 26.83% 55.41%
## 223: 20.35% 60.00% 31.57% 8.12% 26.68% 55.41%
## 224: 20.31% 61.18% 31.57% 8.04% 26.53% 55.41%
## 225: 20.54% 60.00% 32.20% 8.12% 26.97% 55.41%
## 226: 20.74% 61.18% 32.63% 8.20% 27.12% 55.41%
## 227: 20.62% 61.18% 31.99% 8.12% 27.27% 55.41%
## 228: 20.54% 61.18% 31.57% 8.04% 27.42% 55.41%
## 229: 20.86% 61.18% 31.99% 8.60% 27.27% 55.41%
## 230: 20.51% 61.18% 31.57% 8.12% 27.12% 55.41%
## 231: 20.74% 61.18% 31.78% 8.36% 27.42% 55.41%
## 232: 20.74% 61.18% 31.99% 8.20% 27.57% 55.41%
## 233: 20.66% 61.18% 32.20% 8.36% 26.83% 55.41%
## 234: 20.47% 61.18% 31.36% 8.36% 26.68% 55.41%
## 235: 20.58% 61.18% 31.78% 8.44% 26.68% 55.41%
## 236: 20.62% 61.18% 32.20% 8.44% 26.53% 55.41%
## 237: 20.47% 61.18% 31.36% 8.52% 26.38% 55.41%
## 238: 20.58% 61.18% 31.78% 8.36% 26.83% 55.41%
## 239: 20.74% 61.18% 31.99% 8.75% 26.53% 55.41%
## 240: 20.54% 61.18% 31.78% 8.60% 26.23% 55.41%
## 241: 20.62% 61.18% 31.78% 8.68% 26.38% 55.41%
## 242: 20.66% 61.18% 31.78% 8.60% 26.68% 55.41%
## 243: 20.54% 61.18% 31.99% 8.44% 26.38% 55.41%
## 244: 20.31% 61.18% 31.78% 8.04% 26.38% 55.41%
## 245: 20.62% 61.18% 32.20% 8.52% 26.38% 55.41%
## 246: 20.78% 61.18% 32.20% 8.52% 26.97% 55.41%
## 247: 20.51% 61.18% 31.99% 8.20% 26.68% 55.41%
## 248: 20.70% 61.18% 32.42% 8.28% 26.83% 56.76%
## 249: 20.51% 61.18% 31.99% 8.28% 26.53% 55.41%
## 250: 20.58% 61.18% 32.20% 8.20% 26.68% 56.76%
## 251: 20.62% 61.18% 31.99% 8.28% 26.83% 56.76%
## 252: 20.51% 61.18% 31.78% 8.20% 26.83% 55.41%
## 253: 20.58% 61.18% 31.57% 8.36% 27.12% 54.05%
## 254: 20.82% 61.18% 32.42% 8.52% 26.97% 55.41%
## 255: 20.66% 61.18% 31.78% 8.44% 26.97% 55.41%
## 256: 20.62% 60.00% 31.78% 8.60% 26.83% 54.05%
## 257: 20.70% 60.00% 31.78% 8.52% 27.12% 55.41%
## 258: 20.66% 61.18% 31.78% 8.52% 26.83% 55.41%
## 259: 20.35% 60.00% 31.36% 8.52% 26.23% 54.05%
## 260: 20.54% 60.00% 32.20% 8.44% 26.38% 55.41%
## 261: 20.43% 60.00% 31.78% 8.36% 26.53% 54.05%
## 262: 20.54% 60.00% 31.36% 8.75% 26.38% 55.41%
## 263: 20.39% 60.00% 31.57% 8.44% 26.23% 55.41%
## 264: 20.39% 60.00% 31.57% 8.44% 26.23% 55.41%
## 265: 20.39% 60.00% 32.20% 8.20% 26.38% 54.05%
## 266: 20.35% 60.00% 31.99% 8.36% 26.08% 54.05%
## 267: 20.35% 60.00% 31.78% 8.36% 26.08% 55.41%
## 268: 20.43% 60.00% 31.99% 8.44% 26.08% 55.41%
## 269: 20.23% 60.00% 31.57% 8.36% 25.93% 54.05%
## 270: 20.12% 60.00% 31.78% 8.04% 25.93% 54.05%
## 271: 20.04% 60.00% 31.36% 8.12% 25.78% 54.05%
## 272: 20.08% 60.00% 30.93% 8.28% 25.93% 54.05%
## 273: 20.12% 60.00% 31.36% 8.28% 25.78% 54.05%
## 274: 20.00% 60.00% 31.14% 8.20% 25.63% 54.05%
## 275: 20.19% 60.00% 31.36% 8.28% 26.08% 54.05%
## 276: 20.08% 60.00% 31.57% 7.97% 26.08% 54.05%
## 277: 20.27% 60.00% 31.99% 8.28% 25.78% 55.41%
## 278: 20.12% 60.00% 31.57% 8.12% 25.78% 55.41%
## 279: 20.08% 60.00% 31.57% 7.89% 26.08% 55.41%
## 280: 20.04% 60.00% 31.57% 7.81% 26.08% 55.41%
## 281: 20.12% 60.00% 31.78% 7.89% 26.08% 55.41%
## 282: 19.81% 60.00% 31.36% 7.65% 25.78% 54.05%
## 283: 20.19% 60.00% 32.42% 7.81% 26.23% 54.05%
## 284: 19.96% 60.00% 31.57% 7.73% 26.08% 54.05%
## 285: 20.31% 60.00% 32.20% 7.97% 26.53% 54.05%
## 286: 20.27% 60.00% 32.63% 7.65% 26.68% 54.05%
## 287: 20.16% 60.00% 31.78% 7.73% 26.68% 54.05%
## 288: 20.23% 60.00% 32.20% 7.81% 26.68% 52.70%
## 289: 20.23% 60.00% 32.42% 7.89% 26.23% 54.05%
## 290: 20.19% 61.18% 32.42% 7.73% 26.23% 54.05%
## 291: 20.23% 61.18% 32.20% 7.73% 26.68% 52.70%
## 292: 20.19% 61.18% 32.20% 7.73% 26.53% 52.70%
## 293: 20.31% 61.18% 32.63% 7.73% 26.68% 52.70%
## 294: 20.12% 61.18% 32.42% 7.57% 26.38% 52.70%
## 295: 20.19% 61.18% 31.99% 7.73% 26.53% 54.05%
## 296: 20.23% 61.18% 32.20% 7.65% 26.68% 54.05%
## 297: 20.12% 61.18% 31.78% 7.81% 26.38% 52.70%
## 298: 20.27% 61.18% 31.99% 7.97% 26.53% 52.70%
## 299: 20.23% 61.18% 31.78% 7.73% 26.83% 54.05%
## 300: 20.19% 61.18% 31.78% 7.89% 26.53% 52.70%
## 301: 20.27% 61.18% 32.42% 7.81% 26.53% 52.70%
## 302: 20.16% 61.18% 31.99% 7.81% 26.38% 52.70%
## 303: 20.31% 61.18% 31.99% 8.04% 26.53% 52.70%
## 304: 20.27% 61.18% 31.78% 8.04% 26.53% 52.70%
## 305: 20.35% 61.18% 32.42% 7.89% 26.68% 52.70%
## 306: 20.35% 61.18% 32.20% 8.04% 26.53% 52.70%
## 307: 20.23% 61.18% 32.20% 7.97% 26.23% 52.70%
## 308: 20.19% 61.18% 31.99% 7.89% 26.23% 54.05%
## 309: 20.12% 61.18% 31.78% 7.81% 26.23% 54.05%
## 310: 20.23% 61.18% 32.20% 7.81% 26.38% 54.05%
## 311: 20.12% 61.18% 31.99% 7.73% 26.23% 54.05%
## 312: 20.12% 61.18% 32.20% 7.73% 26.08% 54.05%
## 313: 20.27% 61.18% 32.20% 7.97% 26.23% 54.05%
## 314: 20.39% 61.18% 32.63% 7.81% 26.53% 55.41%
## 315: 20.35% 61.18% 32.20% 7.89% 26.53% 55.41%
## 316: 20.31% 61.18% 32.20% 7.89% 26.38% 55.41%
## 317: 20.23% 61.18% 31.99% 7.89% 26.23% 55.41%
## 318: 20.35% 61.18% 31.99% 8.04% 26.38% 55.41%
## 319: 20.27% 61.18% 31.99% 7.97% 26.38% 54.05%
## 320: 20.12% 61.18% 31.57% 7.81% 26.23% 55.41%
## 321: 20.27% 61.18% 31.57% 7.97% 26.53% 55.41%
## 322: 20.16% 61.18% 31.99% 7.81% 26.23% 54.05%
## 323: 20.19% 61.18% 31.57% 7.89% 26.53% 54.05%
## 324: 20.19% 61.18% 32.20% 7.81% 26.23% 54.05%
## 325: 20.23% 61.18% 32.20% 7.89% 26.23% 54.05%
## 326: 20.19% 61.18% 32.63% 7.73% 26.08% 54.05%
## 327: 20.23% 61.18% 32.42% 7.89% 26.08% 54.05%
## 328: 20.16% 61.18% 32.20% 7.81% 25.93% 55.41%
## 329: 20.08% 61.18% 32.20% 7.81% 25.78% 54.05%
## 330: 19.92% 61.18% 31.78% 7.73% 25.63% 54.05%
## 331: 20.00% 61.18% 32.20% 7.73% 25.78% 52.70%
## 332: 20.00% 61.18% 32.42% 7.81% 25.63% 51.35%
## 333: 19.92% 61.18% 32.42% 7.57% 25.78% 51.35%
## 334: 19.84% 61.18% 32.42% 7.57% 25.48% 51.35%
## 335: 19.88% 61.18% 32.20% 7.73% 25.48% 51.35%
## 336: 20.00% 61.18% 32.63% 7.81% 25.48% 51.35%
## 337: 19.88% 61.18% 32.63% 7.65% 25.34% 51.35%
## 338: 20.04% 61.18% 32.63% 7.73% 25.48% 54.05%
## 339: 19.81% 61.18% 32.42% 7.41% 25.48% 52.70%
## 340: 20.00% 61.18% 32.84% 7.57% 25.48% 54.05%
## 341: 19.96% 61.18% 32.42% 7.65% 25.48% 54.05%
## 342: 20.00% 61.18% 32.63% 7.81% 25.19% 54.05%
## 343: 20.19% 61.18% 32.84% 7.81% 25.78% 54.05%
## 344: 19.92% 61.18% 32.63% 7.65% 25.19% 54.05%
## 345: 19.96% 61.18% 32.63% 7.49% 25.48% 55.41%
## 346: 19.84% 61.18% 32.42% 7.49% 25.19% 55.41%
## 347: 20.16% 61.18% 32.20% 7.81% 25.93% 55.41%
## 348: 19.77% 61.18% 31.99% 7.49% 25.19% 55.41%
## 349: 19.77% 61.18% 31.99% 7.49% 25.19% 55.41%
## 350: 19.88% 61.18% 32.20% 7.65% 25.19% 55.41%
## 351: 19.77% 61.18% 31.78% 7.49% 25.48% 54.05%
## 352: 19.84% 61.18% 31.99% 7.41% 25.78% 54.05%
## 353: 19.92% 61.18% 31.99% 7.65% 25.63% 54.05%
## 354: 19.84% 61.18% 31.78% 7.41% 25.93% 54.05%
## 355: 19.84% 61.18% 31.57% 7.41% 26.08% 54.05%
## 356: 20.00% 61.18% 32.20% 7.49% 26.08% 54.05%
## 357: 20.00% 61.18% 32.42% 7.49% 25.93% 54.05%
## 358: 19.96% 61.18% 31.99% 7.49% 26.08% 54.05%
## 359: 20.00% 61.18% 32.20% 7.49% 26.08% 54.05%
## 360: 20.04% 61.18% 32.63% 7.33% 26.23% 54.05%
## 361: 20.00% 61.18% 31.99% 7.41% 26.38% 54.05%
## 362: 19.88% 61.18% 31.78% 7.41% 26.08% 54.05%
## 363: 20.04% 61.18% 32.20% 7.49% 26.23% 54.05%
## 364: 20.04% 61.18% 31.99% 7.57% 26.08% 55.41%
## 365: 20.00% 61.18% 32.42% 7.57% 25.63% 55.41%
## 366: 19.73% 61.18% 31.57% 7.57% 25.34% 54.05%
## 367: 19.92% 61.18% 31.78% 7.65% 25.63% 55.41%
## 368: 20.00% 61.18% 31.78% 7.65% 25.93% 55.41%
## 369: 20.08% 61.18% 31.99% 7.65% 26.08% 55.41%
## 370: 19.96% 61.18% 31.78% 7.73% 25.63% 55.41%
## 371: 19.96% 61.18% 31.57% 7.73% 25.78% 55.41%
## 372: 19.84% 61.18% 31.57% 7.57% 25.63% 55.41%
## 373: 19.92% 61.18% 31.36% 7.57% 26.08% 55.41%
## 374: 19.96% 61.18% 30.93% 7.57% 26.53% 55.41%
## 375: 19.88% 61.18% 30.93% 7.57% 26.23% 55.41%
## 376: 19.84% 61.18% 30.72% 7.73% 25.93% 55.41%
## 377: 19.92% 61.18% 30.93% 7.57% 26.38% 55.41%
## 378: 19.81% 61.18% 30.72% 7.73% 25.78% 55.41%
## 379: 19.73% 61.18% 30.93% 7.73% 25.34% 55.41%
## 380: 19.88% 61.18% 30.72% 7.89% 25.78% 55.41%
## 381: 19.88% 61.18% 31.14% 7.89% 25.48% 55.41%
## 382: 19.81% 61.18% 31.14% 7.81% 25.34% 55.41%
## 383: 19.81% 61.18% 30.93% 7.97% 25.19% 55.41%
## 384: 19.92% 61.18% 30.72% 8.04% 25.63% 55.41%
## 385: 20.12% 61.18% 31.78% 8.04% 25.63% 55.41%
## 386: 19.77% 61.18% 30.72% 7.89% 25.34% 55.41%
## 387: 19.92% 61.18% 31.36% 8.04% 25.19% 55.41%
## 388: 19.84% 61.18% 30.93% 7.97% 25.34% 55.41%
## 389: 19.96% 61.18% 31.14% 8.04% 25.48% 55.41%
## 390: 19.96% 61.18% 31.36% 7.89% 25.63% 55.41%
## 391: 20.00% 61.18% 31.36% 7.97% 25.63% 55.41%
## 392: 19.96% 61.18% 31.14% 8.04% 25.48% 55.41%
## 393: 20.04% 61.18% 31.14% 8.04% 25.78% 55.41%
## 394: 20.04% 61.18% 31.36% 7.89% 25.93% 55.41%
## 395: 19.96% 61.18% 31.36% 7.97% 25.48% 55.41%
## 396: 20.12% 61.18% 31.57% 7.97% 25.93% 55.41%
## 397: 19.96% 61.18% 31.36% 7.89% 25.63% 55.41%
## 398: 19.92% 61.18% 31.14% 7.97% 25.48% 55.41%
## 399: 19.96% 61.18% 30.93% 7.97% 25.78% 55.41%
## 400: 20.08% 62.35% 31.36% 7.97% 25.78% 55.41%
## 401: 19.92% 61.18% 31.57% 7.89% 25.34% 55.41%
## 402: 19.84% 61.18% 31.57% 7.89% 25.04% 55.41%
## 403: 19.96% 61.18% 31.78% 7.89% 25.34% 55.41%
## 404: 19.96% 61.18% 31.99% 7.89% 25.19% 55.41%
## 405: 19.92% 61.18% 31.78% 7.81% 25.34% 55.41%
## 406: 19.88% 61.18% 31.78% 7.73% 25.34% 55.41%
## 407: 20.00% 61.18% 31.99% 7.81% 25.48% 55.41%
## 408: 20.00% 61.18% 32.42% 7.81% 25.19% 55.41%
## 409: 20.00% 61.18% 31.99% 7.89% 25.34% 55.41%
## 410: 19.92% 61.18% 31.78% 7.89% 25.19% 55.41%
## 411: 19.92% 61.18% 32.42% 7.73% 25.04% 55.41%
## 412: 19.92% 61.18% 32.20% 7.89% 24.89% 55.41%
## 413: 19.92% 61.18% 31.99% 7.89% 25.04% 55.41%
## 414: 20.00% 61.18% 32.20% 7.89% 25.19% 55.41%
## 415: 20.00% 61.18% 31.99% 7.89% 25.34% 55.41%
## 416: 20.00% 61.18% 31.99% 7.81% 25.48% 55.41%
## 417: 20.08% 61.18% 32.42% 7.81% 25.48% 55.41%
## 418: 20.00% 61.18% 31.99% 7.89% 25.34% 55.41%
## 419: 20.04% 61.18% 31.99% 7.89% 25.48% 55.41%
## 420: 20.04% 61.18% 32.42% 7.81% 25.34% 55.41%
## 421: 20.00% 61.18% 31.99% 7.81% 25.48% 55.41%
## 422: 20.00% 61.18% 31.99% 7.81% 25.48% 55.41%
## 423: 19.96% 61.18% 31.36% 7.89% 25.63% 55.41%
## 424: 20.12% 61.18% 31.57% 8.04% 25.78% 55.41%
## 425: 20.16% 62.35% 32.20% 7.89% 25.63% 55.41%
## 426: 20.19% 62.35% 32.20% 7.89% 25.78% 55.41%
## 427: 20.16% 61.18% 31.99% 7.97% 25.78% 55.41%
## 428: 20.12% 61.18% 31.99% 7.89% 25.78% 55.41%
## 429: 20.08% 62.35% 31.99% 7.81% 25.63% 55.41%
## 430: 20.08% 62.35% 31.57% 7.89% 25.78% 55.41%
## 431: 19.88% 62.35% 31.14% 7.57% 25.93% 55.41%
## 432: 19.96% 62.35% 31.14% 7.81% 25.78% 55.41%
## 433: 20.00% 62.35% 31.57% 7.81% 25.63% 55.41%
## 434: 19.96% 61.18% 31.14% 7.81% 25.93% 55.41%
## 435: 19.92% 61.18% 31.36% 7.73% 25.78% 55.41%
## 436: 19.96% 62.35% 31.36% 7.81% 25.63% 55.41%
## 437: 19.92% 62.35% 31.36% 7.81% 25.48% 55.41%
## 438: 20.00% 62.35% 31.57% 7.89% 25.48% 55.41%
## 439: 19.92% 61.18% 31.57% 7.89% 25.34% 55.41%
## 440: 19.88% 61.18% 31.14% 7.89% 25.48% 55.41%
## 441: 19.92% 61.18% 31.36% 7.81% 25.63% 55.41%
## 442: 19.81% 62.35% 31.14% 7.81% 25.34% 54.05%
## 443: 19.84% 61.18% 31.78% 7.73% 25.34% 54.05%
## 444: 19.81% 61.18% 31.36% 7.81% 25.34% 54.05%
## 445: 19.84% 61.18% 31.36% 7.73% 25.63% 54.05%
## 446: 19.81% 61.18% 31.36% 7.65% 25.63% 54.05%
## 447: 19.84% 61.18% 31.36% 7.73% 25.63% 54.05%
## 448: 19.92% 61.18% 31.57% 7.81% 25.63% 54.05%
## 449: 19.92% 61.18% 31.57% 7.81% 25.63% 54.05%
## 450: 19.84% 61.18% 31.14% 7.81% 25.63% 54.05%
## 451: 19.81% 61.18% 31.14% 7.73% 25.63% 54.05%
## 452: 19.77% 60.00% 30.93% 7.81% 25.63% 54.05%
## 453: 19.81% 60.00% 30.93% 7.81% 25.78% 54.05%
## 454: 19.81% 60.00% 30.93% 7.81% 25.78% 54.05%
## 455: 20.00% 60.00% 31.57% 7.97% 25.78% 54.05%
## 456: 20.08% 60.00% 31.78% 8.04% 25.78% 54.05%
## 457: 19.96% 60.00% 31.57% 7.89% 25.78% 54.05%
## 458: 20.08% 60.00% 31.36% 8.12% 25.93% 54.05%
## 459: 20.00% 60.00% 31.57% 7.89% 25.93% 54.05%
## 460: 19.65% 60.00% 30.93% 7.57% 25.63% 54.05%
## 461: 19.84% 60.00% 31.14% 7.97% 25.48% 54.05%
## 462: 19.81% 60.00% 31.36% 7.73% 25.63% 54.05%
## 463: 19.88% 60.00% 31.36% 7.89% 25.63% 54.05%
## 464: 19.92% 60.00% 31.36% 7.81% 25.93% 54.05%
## 465: 19.84% 60.00% 31.36% 7.73% 25.78% 54.05%
## 466: 19.77% 60.00% 31.36% 7.65% 25.78% 52.70%
## 467: 19.81% 61.18% 31.57% 7.57% 25.78% 52.70%
## 468: 19.81% 61.18% 31.36% 7.57% 25.78% 54.05%
## 469: 19.84% 61.18% 31.57% 7.65% 25.78% 52.70%
## 470: 19.92% 61.18% 31.78% 7.65% 25.93% 52.70%
## 471: 19.96% 61.18% 31.99% 7.73% 25.78% 52.70%
## 472: 19.96% 61.18% 32.20% 7.65% 25.78% 52.70%
## 473: 19.96% 61.18% 32.42% 7.49% 25.93% 52.70%
## 474: 19.88% 61.18% 32.20% 7.49% 25.78% 52.70%
## 475: 19.77% 61.18% 32.20% 7.57% 25.19% 52.70%
## 476: 19.96% 61.18% 31.99% 7.65% 25.93% 52.70%
## 477: 19.77% 61.18% 31.57% 7.57% 25.63% 52.70%
## 478: 19.84% 61.18% 31.99% 7.73% 25.34% 52.70%
## 479: 19.92% 61.18% 31.78% 7.81% 25.63% 52.70%
## 480: 19.84% 61.18% 31.57% 7.73% 25.63% 52.70%
## 481: 19.96% 62.35% 31.78% 7.73% 25.78% 52.70%
## 482: 19.88% 62.35% 31.36% 7.65% 25.93% 52.70%
## 483: 19.81% 61.18% 31.57% 7.65% 25.63% 52.70%
## 484: 19.84% 61.18% 31.57% 7.65% 25.78% 52.70%
## 485: 19.65% 61.18% 31.36% 7.49% 25.48% 52.70%
## 486: 19.73% 61.18% 31.36% 7.65% 25.48% 52.70%
## 487: 19.73% 61.18% 31.36% 7.57% 25.63% 52.70%
## 488: 19.81% 61.18% 31.36% 7.73% 25.63% 52.70%
## 489: 19.73% 61.18% 31.36% 7.65% 25.48% 52.70%
## 490: 19.69% 61.18% 31.36% 7.65% 25.34% 52.70%
## 491: 19.81% 61.18% 31.36% 7.73% 25.63% 52.70%
## 492: 19.69% 61.18% 31.36% 7.49% 25.63% 52.70%
## 493: 19.69% 61.18% 31.14% 7.57% 25.63% 52.70%
## 494: 19.69% 61.18% 31.36% 7.57% 25.48% 52.70%
## 495: 19.69% 61.18% 31.14% 7.57% 25.63% 52.70%
## 496: 19.69% 61.18% 31.14% 7.49% 25.78% 52.70%
## 497: 19.73% 61.18% 31.14% 7.65% 25.63% 52.70%
## 498: 19.73% 61.18% 31.36% 7.57% 25.63% 52.70%
## 499: 19.61% 61.18% 31.36% 7.49% 25.34% 52.70%
## 500: 19.65% 61.18% 31.57% 7.49% 25.34% 52.70%test1 <- predict(model_rf1, newdata = test)
table(test1, test$Hospital.overall.rating)##
## test1 1 2 3 4 5
## 1 15 6 0 0 0
## 2 17 138 17 0 0
## 3 0 68 466 65 0
## 4 0 0 21 228 24
## 5 0 0 0 0 13# test1 1 2 3 4 5
# 1 15 6 0 0 0
# 2 17 138 17 0 0
# 3 0 68 466 65 0
# 4 0 0 21 228 24
# 5 0 0 0 0 13
summary(model_rf1)## Length Class Mode
## call 8 -none- call
## type 1 -none- character
## predicted 2570 factor numeric
## err.rate 3000 -none- numeric
## confusion 30 -none- numeric
## votes 12850 matrix numeric
## oob.times 2570 -none- numeric
## classes 5 -none- character
## importance 53 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 2570 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## terms 3 terms callconf_matrix1 <- confusionMatrix(test1, test$Hospital.overall.rating, positive = "Yes")
conf_matrix1## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5
## 1 15 6 0 0 0
## 2 17 138 17 0 0
## 3 0 68 466 65 0
## 4 0 0 21 228 24
## 5 0 0 0 0 13
##
## Overall Statistics
##
## Accuracy : 0.7978
## 95% CI : (0.7725, 0.8214)
## No Information Rate : 0.4675
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6835
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
## Sensitivity 0.46875 0.6509 0.9246 0.7782 0.35135
## Specificity 0.99426 0.9607 0.7683 0.9427 1.00000
## Pos Pred Value 0.71429 0.8023 0.7780 0.8352 1.00000
## Neg Pred Value 0.98392 0.9183 0.9207 0.9193 0.97746
## Prevalence 0.02968 0.1967 0.4675 0.2718 0.03432
## Detection Rate 0.01391 0.1280 0.4323 0.2115 0.01206
## Detection Prevalence 0.01948 0.1596 0.5557 0.2532 0.01206
## Balanced Accuracy 0.73151 0.8058 0.8464 0.8604 0.67568# Confusion Matrix and Statistics
#
# Reference
# Prediction 1 2 3 4 5
# 1 15 6 0 0 0
# 2 17 138 17 0 0
# 3 0 68 466 65 0
# 4 0 0 21 228 24
# 5 0 0 0 0 13
#
# Overall Statistics
#
# Accuracy : 0.7978
# 95% CI : (0.7725, 0.8214)
# No Information Rate : 0.4675
# P-Value [Acc > NIR] : < 2.2e-16
#
# Kappa : 0.6835
# Mcnemar's Test P-Value : NA
#
# Statistics by Class:
#
# Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
# Sensitivity 0.46875 0.6509 0.9246 0.7782 0.35135
# Specificity 0.99426 0.9607 0.7683 0.9427 1.00000
# Pos Pred Value 0.71429 0.8023 0.7780 0.8352 1.00000
# Neg Pred Value 0.98392 0.9183 0.9207 0.9193 0.97746
# Prevalence 0.02968 0.1967 0.4675 0.2718 0.03432
# Detection Rate 0.01391 0.1280 0.4323 0.2115 0.01206
# Detection Prevalence 0.01948 0.1596 0.5557 0.2532 0.01206
# Balanced Accuracy 0.73151 0.8058 0.8464 0.8604 0.67568
# Accuracy is 79.8%
model_rf2 <- randomForest(Hospital.overall.rating ~ ., data = train, promiximity = FALSE, ntree = 1000, mtry = 20, do.trace = TRUE, na.action = na.omit)## ntree OOB 1 2 3 4 5
## 1: 37.38% 69.23% 48.82% 31.65% 36.69% 41.38%
## 2: 38.81% 52.17% 46.76% 33.20% 42.23% 42.55%
## 3: 36.65% 55.00% 45.19% 29.83% 41.24% 42.11%
## 4: 36.06% 52.86% 48.60% 28.06% 38.28% 55.74%
## 5: 35.12% 53.95% 45.26% 28.36% 36.35% 52.11%
## 6: 34.63% 53.85% 45.43% 26.53% 38.24% 49.32%
## 7: 33.25% 55.13% 43.83% 25.68% 35.09% 52.70%
## 8: 32.52% 52.50% 42.70% 24.76% 35.01% 54.05%
## 9: 32.65% 61.45% 43.38% 23.76% 35.95% 52.70%
## 10: 31.33% 51.81% 42.64% 22.81% 34.64% 51.35%
## 11: 30.94% 54.22% 41.36% 22.43% 34.48% 51.35%
## 12: 31.25% 52.38% 43.83% 22.33% 34.38% 51.35%
## 13: 29.89% 59.52% 40.76% 20.57% 33.43% 54.05%
## 14: 29.20% 57.14% 40.55% 19.51% 33.73% 50.00%
## 15: 28.83% 56.47% 39.07% 18.88% 34.43% 51.35%
## 16: 27.85% 55.29% 38.00% 17.54% 33.68% 55.41%
## 17: 27.41% 55.29% 38.98% 16.67% 33.38% 51.35%
## 18: 27.88% 57.65% 39.19% 17.38% 32.94% 55.41%
## 19: 26.29% 55.29% 36.86% 15.17% 32.94% 55.41%
## 20: 26.55% 56.47% 37.71% 15.47% 32.49% 56.76%
## 21: 26.16% 57.65% 38.14% 14.84% 31.89% 55.41%
## 22: 25.38% 55.29% 35.38% 14.84% 31.15% 55.41%
## 23: 24.99% 50.59% 36.23% 14.05% 31.00% 56.76%
## 24: 25.07% 51.76% 38.35% 13.50% 30.70% 56.76%
## 25: 24.63% 52.94% 36.23% 13.72% 29.51% 60.81%
## 26: 24.71% 52.94% 35.81% 13.56% 30.55% 59.46%
## 27: 24.75% 52.94% 36.86% 13.56% 29.81% 60.81%
## 28: 24.05% 56.47% 35.59% 12.54% 29.81% 58.11%
## 29: 23.97% 55.29% 35.17% 12.62% 29.51% 60.81%
## 30: 24.20% 57.65% 35.81% 13.09% 29.51% 54.05%
## 31: 24.24% 58.82% 36.86% 13.33% 28.02% 56.76%
## 32: 24.12% 58.82% 36.65% 12.85% 28.61% 56.76%
## 33: 23.93% 58.82% 35.38% 12.85% 29.06% 54.05%
## 34: 23.42% 58.82% 35.59% 12.62% 27.57% 52.70%
## 35: 23.27% 58.82% 35.59% 12.46% 27.27% 52.70%
## 36: 23.19% 60.00% 35.59% 11.67% 28.02% 55.41%
## 37: 23.77% 58.82% 36.86% 12.54% 28.17% 52.70%
## 38: 23.46% 60.00% 35.38% 12.22% 28.17% 55.41%
## 39: 23.62% 58.82% 35.81% 12.22% 28.91% 52.70%
## 40: 23.31% 55.29% 34.11% 12.22% 29.36% 52.70%
## 41: 23.39% 56.47% 35.17% 12.15% 29.06% 51.35%
## 42: 23.15% 58.82% 34.32% 11.99% 28.76% 51.35%
## 43: 22.57% 57.65% 34.53% 11.20% 28.17% 50.00%
## 44: 23.11% 58.82% 35.17% 11.67% 28.46% 52.70%
## 45: 22.84% 60.00% 34.96% 11.44% 28.02% 51.35%
## 46: 22.72% 60.00% 35.17% 11.59% 27.12% 51.35%
## 47: 22.80% 60.00% 35.81% 11.12% 27.57% 54.05%
## 48: 22.72% 60.00% 35.81% 10.96% 27.87% 51.35%
## 49: 22.30% 57.65% 34.32% 11.28% 26.83% 52.70%
## 50: 22.45% 58.82% 34.75% 10.88% 27.72% 52.70%
## 51: 22.30% 58.82% 34.32% 11.20% 26.97% 51.35%
## 52: 22.72% 60.00% 34.75% 11.36% 27.72% 52.70%
## 53: 22.45% 60.00% 33.90% 11.36% 27.12% 54.05%
## 54: 22.41% 60.00% 34.32% 11.59% 26.23% 54.05%
## 55: 22.33% 60.00% 33.69% 11.51% 26.83% 51.35%
## 56: 22.06% 58.82% 33.26% 10.96% 27.27% 51.35%
## 57: 22.26% 60.00% 33.26% 11.12% 27.27% 54.05%
## 58: 22.18% 58.82% 32.42% 11.36% 27.27% 54.05%
## 59: 22.18% 60.00% 31.36% 11.44% 27.72% 54.05%
## 60: 21.95% 60.00% 31.57% 10.80% 27.72% 55.41%
## 61: 22.18% 60.00% 31.99% 11.51% 26.97% 55.41%
## 62: 21.63% 60.00% 31.36% 10.80% 26.53% 56.76%
## 63: 21.56% 58.82% 31.57% 10.73% 26.38% 56.76%
## 64: 21.48% 57.65% 31.78% 10.41% 26.68% 56.76%
## 65: 21.87% 58.82% 31.36% 10.88% 27.42% 56.76%
## 66: 21.40% 60.00% 30.93% 10.49% 26.68% 55.41%
## 67: 21.44% 57.65% 30.72% 10.65% 26.83% 56.76%
## 68: 21.05% 56.47% 30.08% 10.25% 26.53% 58.11%
## 69: 21.13% 56.47% 29.66% 10.65% 26.68% 55.41%
## 70: 21.13% 56.47% 30.51% 10.49% 26.38% 55.41%
## 71: 21.13% 56.47% 30.08% 10.57% 26.68% 54.05%
## 72: 21.44% 60.00% 30.93% 10.65% 26.23% 58.11%
## 73: 21.48% 58.82% 30.93% 10.57% 26.68% 58.11%
## 74: 21.28% 57.65% 30.93% 10.49% 26.38% 56.76%
## 75: 21.17% 57.65% 31.36% 9.86% 26.68% 58.11%
## 76: 21.56% 58.82% 30.51% 10.41% 27.72% 56.76%
## 77: 21.48% 58.82% 30.72% 10.49% 26.97% 58.11%
## 78: 21.48% 60.00% 30.51% 10.49% 27.27% 55.41%
## 79: 21.40% 60.00% 30.72% 10.17% 27.27% 56.76%
## 80: 21.71% 61.18% 31.99% 10.33% 26.97% 58.11%
## 81: 21.56% 61.18% 31.78% 10.09% 27.12% 56.76%
## 82: 21.32% 61.18% 31.78% 9.78% 26.83% 56.76%
## 83: 21.52% 62.35% 31.36% 9.78% 27.57% 58.11%
## 84: 21.63% 62.35% 31.99% 9.86% 27.42% 58.11%
## 85: 22.10% 62.35% 32.84% 10.41% 27.57% 58.11%
## 86: 21.79% 61.18% 32.42% 10.25% 27.27% 56.76%
## 87: 21.63% 62.35% 31.99% 10.17% 27.12% 55.41%
## 88: 21.32% 62.35% 31.57% 9.78% 26.97% 55.41%
## 89: 21.44% 62.35% 31.78% 9.78% 27.12% 56.76%
## 90: 21.05% 58.82% 30.93% 9.62% 27.12% 55.41%
## 91: 21.36% 61.18% 31.78% 9.62% 27.57% 54.05%
## 92: 21.28% 62.35% 30.93% 9.70% 27.57% 54.05%
## 93: 21.48% 61.18% 31.57% 9.70% 27.87% 55.41%
## 94: 21.13% 62.35% 31.36% 9.62% 26.83% 54.05%
## 95: 21.36% 61.18% 31.36% 10.02% 27.27% 52.70%
## 96: 21.01% 61.18% 31.36% 9.54% 26.83% 52.70%
## 97: 21.17% 60.00% 31.14% 9.94% 26.97% 52.70%
## 98: 21.13% 61.18% 30.93% 9.86% 26.83% 54.05%
## 99: 21.05% 61.18% 31.36% 9.70% 26.53% 54.05%
## 100: 21.36% 58.82% 31.99% 9.86% 27.42% 52.70%
## 101: 20.97% 57.65% 32.42% 9.46% 26.53% 52.70%
## 102: 21.05% 57.65% 31.99% 9.38% 27.27% 52.70%
## 103: 20.86% 57.65% 31.99% 9.31% 26.68% 52.70%
## 104: 20.70% 57.65% 31.14% 9.23% 26.83% 52.70%
## 105: 20.89% 57.65% 31.57% 9.38% 26.97% 52.70%
## 106: 20.78% 57.65% 31.78% 9.23% 26.53% 54.05%
## 107: 21.01% 60.00% 31.78% 9.54% 26.53% 54.05%
## 108: 20.86% 60.00% 31.14% 9.46% 26.68% 52.70%
## 109: 21.05% 60.00% 31.78% 9.78% 26.38% 52.70%
## 110: 21.01% 60.00% 31.57% 9.54% 26.83% 52.70%
## 111: 20.97% 60.00% 31.78% 9.46% 26.68% 52.70%
## 112: 20.97% 60.00% 31.78% 9.62% 26.53% 51.35%
## 113: 20.82% 60.00% 31.14% 9.46% 26.68% 51.35%
## 114: 20.54% 60.00% 31.36% 9.07% 26.23% 51.35%
## 115: 20.51% 61.18% 31.36% 8.75% 26.53% 51.35%
## 116: 20.39% 61.18% 30.72% 8.91% 26.23% 51.35%
## 117: 20.70% 61.18% 31.14% 8.99% 26.97% 51.35%
## 118: 20.35% 61.18% 30.30% 8.60% 26.97% 51.35%
## 119: 20.39% 60.00% 30.51% 8.60% 27.12% 51.35%
## 120: 20.35% 58.82% 30.93% 8.44% 27.12% 51.35%
## 121: 20.31% 58.82% 30.51% 8.60% 26.97% 51.35%
## 122: 20.58% 58.82% 31.14% 8.83% 26.97% 52.70%
## 123: 20.35% 58.82% 30.72% 8.75% 26.68% 51.35%
## 124: 20.47% 58.82% 30.30% 8.83% 27.27% 51.35%
## 125: 20.70% 61.18% 30.72% 8.99% 27.12% 52.70%
## 126: 20.74% 61.18% 30.93% 8.99% 27.12% 52.70%
## 127: 20.66% 61.18% 30.30% 9.23% 26.83% 52.70%
## 128: 20.31% 61.18% 30.30% 8.83% 26.23% 52.70%
## 129: 20.54% 61.18% 30.51% 9.31% 26.08% 52.70%
## 130: 20.74% 61.18% 30.72% 9.46% 26.23% 54.05%
## 131: 20.62% 62.35% 30.08% 9.38% 26.23% 54.05%
## 132: 20.54% 62.35% 30.08% 9.23% 26.23% 54.05%
## 133: 20.43% 62.35% 30.51% 8.99% 25.93% 54.05%
## 134: 20.43% 62.35% 30.30% 9.15% 25.78% 54.05%
## 135: 20.54% 62.35% 30.51% 9.15% 26.08% 54.05%
## 136: 19.96% 62.35% 30.08% 8.52% 25.48% 52.70%
## 137: 20.19% 63.53% 29.87% 8.91% 25.63% 52.70%
## 138: 20.19% 63.53% 30.08% 8.83% 25.63% 52.70%
## 139: 20.04% 62.35% 30.08% 8.75% 25.34% 52.70%
## 140: 20.04% 61.18% 30.30% 8.52% 25.78% 52.70%
## 141: 19.96% 62.35% 29.87% 8.36% 25.93% 52.70%
## 142: 19.96% 61.18% 30.30% 8.36% 25.78% 52.70%
## 143: 20.12% 61.18% 30.08% 8.75% 25.78% 52.70%
## 144: 19.92% 61.18% 30.51% 8.28% 25.63% 52.70%
## 145: 20.04% 62.35% 30.51% 8.36% 25.78% 52.70%
## 146: 20.04% 61.18% 30.30% 8.52% 25.78% 52.70%
## 147: 20.08% 62.35% 30.72% 8.36% 25.78% 52.70%
## 148: 19.92% 61.18% 30.08% 8.28% 25.93% 52.70%
## 149: 19.77% 61.18% 30.08% 8.12% 25.63% 52.70%
## 150: 20.04% 61.18% 30.93% 8.28% 25.78% 52.70%
## 151: 19.92% 61.18% 30.30% 8.04% 26.23% 52.70%
## 152: 19.96% 61.18% 30.72% 8.12% 25.93% 52.70%
## 153: 19.96% 61.18% 30.72% 8.20% 25.93% 51.35%
## 154: 19.81% 61.18% 30.93% 8.04% 25.63% 50.00%
## 155: 19.96% 62.35% 30.72% 8.12% 25.78% 52.70%
## 156: 20.12% 61.18% 31.57% 8.20% 25.93% 51.35%
## 157: 20.31% 62.35% 31.57% 8.44% 25.93% 52.70%
## 158: 19.92% 61.18% 31.36% 7.97% 25.63% 52.70%
## 159: 20.16% 62.35% 31.14% 8.36% 25.93% 51.35%
## 160: 20.04% 62.35% 31.78% 8.04% 25.63% 51.35%
## 161: 20.23% 61.18% 31.36% 8.36% 25.78% 55.41%
## 162: 20.27% 61.18% 31.36% 8.52% 25.78% 54.05%
## 163: 20.31% 61.18% 31.57% 8.60% 25.48% 55.41%
## 164: 20.39% 61.18% 31.57% 8.60% 25.78% 55.41%
## 165: 20.31% 61.18% 31.36% 8.68% 25.63% 54.05%
## 166: 20.31% 61.18% 31.99% 8.52% 25.63% 52.70%
## 167: 20.39% 61.18% 31.78% 8.75% 25.78% 51.35%
## 168: 20.47% 60.00% 32.42% 8.83% 25.48% 52.70%
## 169: 20.27% 61.18% 31.78% 8.60% 25.48% 52.70%
## 170: 20.23% 61.18% 31.78% 8.52% 25.48% 52.70%
## 171: 20.23% 60.00% 31.99% 8.44% 25.63% 52.70%
## 172: 20.31% 60.00% 31.78% 8.52% 25.93% 52.70%
## 173: 20.31% 60.00% 32.20% 8.52% 25.63% 52.70%
## 174: 20.19% 60.00% 31.99% 8.36% 25.48% 54.05%
## 175: 20.23% 60.00% 31.99% 8.28% 25.78% 54.05%
## 176: 20.23% 60.00% 31.78% 8.44% 25.78% 52.70%
## 177: 20.23% 60.00% 32.20% 8.28% 25.78% 52.70%
## 178: 20.35% 60.00% 32.63% 8.44% 25.63% 52.70%
## 179: 20.16% 58.82% 31.99% 8.44% 25.48% 52.70%
## 180: 20.27% 60.00% 31.99% 8.60% 25.48% 52.70%
## 181: 20.31% 60.00% 31.99% 8.52% 25.78% 52.70%
## 182: 20.43% 60.00% 31.78% 8.75% 25.78% 54.05%
## 183: 20.27% 60.00% 31.36% 8.68% 25.63% 54.05%
## 184: 20.27% 57.65% 31.99% 8.60% 25.78% 52.70%
## 185: 20.47% 61.18% 31.99% 8.68% 25.78% 54.05%
## 186: 20.43% 60.00% 32.20% 8.68% 25.63% 54.05%
## 187: 20.27% 58.82% 31.78% 8.68% 25.48% 54.05%
## 188: 20.27% 60.00% 32.20% 8.52% 25.48% 52.70%
## 189: 20.27% 60.00% 31.57% 8.68% 25.63% 52.70%
## 190: 20.43% 60.00% 31.99% 8.91% 25.48% 52.70%
## 191: 20.16% 60.00% 31.36% 8.44% 25.63% 54.05%
## 192: 20.19% 58.82% 31.57% 8.44% 25.78% 54.05%
## 193: 20.08% 60.00% 31.78% 8.28% 25.34% 54.05%
## 194: 20.23% 60.00% 31.99% 8.36% 25.63% 54.05%
## 195: 20.08% 58.82% 31.36% 8.44% 25.48% 54.05%
## 196: 20.23% 58.82% 31.57% 8.60% 25.63% 54.05%
## 197: 20.08% 60.00% 31.57% 8.36% 25.34% 54.05%
## 198: 20.04% 60.00% 31.57% 8.20% 25.48% 54.05%
## 199: 20.08% 60.00% 31.78% 8.28% 25.34% 54.05%
## 200: 20.04% 60.00% 31.57% 8.20% 25.48% 54.05%
## 201: 20.31% 61.18% 32.42% 8.36% 25.63% 52.70%
## 202: 20.16% 60.00% 31.78% 8.20% 25.78% 54.05%
## 203: 20.04% 60.00% 31.78% 8.04% 25.63% 54.05%
## 204: 20.16% 60.00% 31.78% 8.28% 25.63% 54.05%
## 205: 20.27% 61.18% 31.99% 8.20% 25.93% 54.05%
## 206: 20.16% 61.18% 31.36% 8.28% 25.78% 54.05%
## 207: 19.92% 60.00% 31.36% 8.12% 25.48% 52.70%
## 208: 20.04% 60.00% 31.78% 8.28% 25.34% 52.70%
## 209: 20.04% 61.18% 31.57% 8.28% 25.34% 52.70%
## 210: 20.16% 60.00% 31.78% 8.28% 25.63% 54.05%
## 211: 20.04% 61.18% 31.78% 8.04% 25.48% 54.05%
## 212: 20.04% 61.18% 32.20% 7.97% 25.48% 52.70%
## 213: 19.96% 61.18% 31.99% 8.04% 25.19% 52.70%
## 214: 20.00% 61.18% 32.20% 7.89% 25.48% 52.70%
## 215: 19.92% 61.18% 31.57% 7.97% 25.48% 52.70%
## 216: 20.00% 61.18% 31.78% 8.04% 25.48% 52.70%
## 217: 19.92% 61.18% 31.99% 7.81% 25.48% 52.70%
## 218: 19.81% 61.18% 31.78% 7.65% 25.48% 52.70%
## 219: 20.04% 62.35% 31.99% 7.97% 25.48% 52.70%
## 220: 20.04% 61.18% 32.20% 7.89% 25.63% 52.70%
## 221: 19.88% 62.35% 31.78% 7.97% 25.04% 52.70%
## 222: 19.96% 62.35% 31.99% 7.81% 25.34% 54.05%
## 223: 19.96% 62.35% 31.99% 7.89% 25.19% 54.05%
## 224: 20.04% 62.35% 31.99% 7.81% 25.63% 54.05%
## 225: 20.08% 63.53% 31.57% 7.89% 25.78% 54.05%
## 226: 20.00% 63.53% 31.36% 8.04% 25.34% 54.05%
## 227: 20.04% 64.71% 31.36% 7.97% 25.48% 54.05%
## 228: 20.00% 63.53% 31.36% 8.04% 25.34% 54.05%
## 229: 20.04% 63.53% 31.36% 7.97% 25.63% 54.05%
## 230: 20.16% 62.35% 31.57% 8.04% 25.93% 54.05%
## 231: 20.27% 63.53% 31.78% 7.97% 26.23% 54.05%
## 232: 20.16% 63.53% 32.20% 7.89% 25.63% 54.05%
## 233: 20.19% 63.53% 32.20% 7.97% 25.63% 54.05%
## 234: 20.35% 63.53% 32.42% 8.04% 25.93% 54.05%
## 235: 20.08% 63.53% 31.36% 8.04% 25.63% 54.05%
## 236: 19.92% 63.53% 31.36% 8.04% 25.04% 54.05%
## 237: 20.16% 63.53% 31.99% 8.04% 25.48% 54.05%
## 238: 20.16% 63.53% 31.78% 8.28% 25.19% 54.05%
## 239: 20.16% 63.53% 31.99% 8.20% 25.19% 54.05%
## 240: 20.27% 63.53% 31.99% 8.20% 25.63% 54.05%
## 241: 20.23% 63.53% 31.78% 8.36% 25.34% 54.05%
## 242: 20.23% 63.53% 31.99% 8.36% 25.19% 54.05%
## 243: 20.39% 63.53% 31.57% 8.44% 25.93% 54.05%
## 244: 20.35% 63.53% 31.78% 8.44% 25.63% 54.05%
## 245: 20.08% 63.53% 31.57% 8.04% 25.48% 54.05%
## 246: 19.92% 63.53% 31.36% 7.81% 25.48% 54.05%
## 247: 20.08% 63.53% 31.57% 8.20% 25.19% 54.05%
## 248: 20.12% 63.53% 31.57% 8.20% 25.34% 54.05%
## 249: 20.12% 63.53% 31.78% 8.20% 25.19% 54.05%
## 250: 20.19% 63.53% 31.78% 8.28% 25.34% 54.05%
## 251: 20.16% 63.53% 31.99% 8.20% 25.19% 54.05%
## 252: 20.23% 63.53% 31.57% 8.52% 25.19% 54.05%
## 253: 20.00% 63.53% 31.36% 8.20% 25.04% 54.05%
## 254: 19.96% 63.53% 30.93% 8.20% 25.19% 54.05%
## 255: 20.00% 63.53% 31.36% 8.28% 24.89% 54.05%
## 256: 20.16% 63.53% 31.78% 8.12% 25.48% 54.05%
## 257: 20.19% 63.53% 31.57% 8.36% 25.34% 54.05%
## 258: 20.27% 63.53% 31.57% 8.36% 25.63% 54.05%
## 259: 20.23% 63.53% 31.57% 8.28% 25.63% 54.05%
## 260: 20.23% 63.53% 31.57% 8.44% 25.34% 54.05%
## 261: 20.16% 63.53% 32.20% 8.04% 25.34% 54.05%
## 262: 20.12% 63.53% 31.36% 8.28% 25.34% 54.05%
## 263: 20.12% 63.53% 31.14% 8.44% 25.19% 54.05%
## 264: 20.19% 62.35% 31.36% 8.44% 25.48% 54.05%
## 265: 20.00% 62.35% 30.72% 8.20% 25.63% 54.05%
## 266: 20.04% 62.35% 30.93% 8.28% 25.48% 54.05%
## 267: 20.04% 62.35% 31.36% 8.20% 25.34% 54.05%
## 268: 20.08% 62.35% 31.36% 8.12% 25.63% 54.05%
## 269: 20.12% 62.35% 31.36% 8.20% 25.63% 54.05%
## 270: 20.00% 61.18% 31.36% 8.20% 25.34% 54.05%
## 271: 19.96% 61.18% 31.14% 8.20% 25.34% 54.05%
## 272: 20.08% 61.18% 31.14% 8.28% 25.63% 54.05%
## 273: 20.08% 62.35% 31.14% 8.20% 25.63% 54.05%
## 274: 20.16% 62.35% 31.99% 8.12% 25.48% 54.05%
## 275: 19.92% 61.18% 30.93% 8.12% 25.48% 54.05%
## 276: 19.92% 61.18% 30.93% 8.12% 25.48% 54.05%
## 277: 20.00% 61.18% 31.36% 8.20% 25.34% 54.05%
## 278: 20.08% 61.18% 30.93% 8.28% 25.78% 54.05%
## 279: 20.04% 61.18% 31.36% 8.12% 25.63% 54.05%
## 280: 19.96% 61.18% 31.36% 8.12% 25.19% 55.41%
## 281: 20.08% 61.18% 31.57% 8.04% 25.63% 55.41%
## 282: 20.16% 61.18% 31.57% 8.12% 25.78% 55.41%
## 283: 20.16% 61.18% 31.78% 8.04% 25.93% 54.05%
## 284: 20.08% 61.18% 31.57% 8.12% 25.63% 54.05%
## 285: 20.27% 61.18% 31.78% 8.20% 25.93% 55.41%
## 286: 20.35% 61.18% 31.57% 8.28% 26.23% 55.41%
## 287: 20.19% 61.18% 31.57% 8.20% 25.78% 55.41%
## 288: 20.19% 60.00% 31.99% 8.12% 25.93% 54.05%
## 289: 20.31% 60.00% 31.78% 8.20% 26.23% 55.41%
## 290: 20.12% 60.00% 31.36% 8.04% 26.08% 55.41%
## 291: 20.31% 61.18% 31.36% 8.36% 26.08% 55.41%
## 292: 20.19% 62.35% 31.78% 8.12% 25.63% 55.41%
## 293: 20.35% 62.35% 31.57% 8.20% 26.23% 55.41%
## 294: 20.31% 62.35% 31.78% 8.28% 25.78% 55.41%
## 295: 20.27% 62.35% 31.78% 8.28% 25.63% 55.41%
## 296: 20.31% 62.35% 31.57% 8.20% 26.08% 55.41%
## 297: 20.23% 62.35% 31.36% 8.20% 25.93% 55.41%
## 298: 20.35% 62.35% 31.78% 8.20% 26.08% 55.41%
## 299: 20.35% 63.53% 31.99% 8.04% 26.08% 55.41%
## 300: 20.27% 62.35% 31.99% 7.97% 26.08% 55.41%
## 301: 20.23% 62.35% 31.99% 7.97% 25.93% 55.41%
## 302: 20.23% 61.18% 31.99% 8.04% 25.93% 55.41%
## 303: 20.12% 63.53% 31.36% 7.97% 25.78% 55.41%
## 304: 20.08% 61.18% 31.36% 8.20% 25.48% 55.41%
## 305: 19.96% 61.18% 31.36% 7.97% 25.48% 55.41%
## 306: 20.08% 61.18% 31.57% 8.04% 25.63% 55.41%
## 307: 20.04% 60.00% 31.36% 8.04% 25.78% 55.41%
## 308: 19.92% 61.18% 31.57% 7.73% 25.63% 55.41%
## 309: 20.04% 64.71% 31.36% 7.73% 25.78% 55.41%
## 310: 19.92% 61.18% 31.57% 7.65% 25.78% 55.41%
## 311: 19.96% 63.53% 31.57% 7.57% 25.78% 55.41%
## 312: 19.88% 63.53% 31.14% 7.65% 25.63% 55.41%
## 313: 19.92% 62.35% 30.93% 7.73% 25.93% 55.41%
## 314: 19.81% 62.35% 31.14% 7.73% 25.34% 55.41%
## 315: 19.81% 62.35% 30.93% 7.73% 25.48% 55.41%
## 316: 19.96% 64.71% 31.36% 7.73% 25.48% 55.41%
## 317: 19.73% 63.53% 30.72% 7.57% 25.48% 55.41%
## 318: 19.81% 63.53% 31.14% 7.65% 25.34% 55.41%
## 319: 19.84% 62.35% 31.14% 7.81% 25.34% 55.41%
## 320: 19.61% 61.18% 30.72% 7.65% 25.19% 55.41%
## 321: 19.69% 63.53% 30.72% 7.65% 25.19% 55.41%
## 322: 19.81% 63.53% 31.14% 7.73% 25.34% 54.05%
## 323: 19.77% 63.53% 30.72% 7.81% 25.19% 55.41%
## 324: 20.00% 63.53% 31.14% 7.97% 25.63% 54.05%
## 325: 19.96% 63.53% 31.57% 7.81% 25.48% 54.05%
## 326: 20.00% 63.53% 31.36% 7.89% 25.63% 54.05%
## 327: 19.88% 62.35% 30.93% 8.12% 25.19% 54.05%
## 328: 19.84% 62.35% 31.14% 7.73% 25.63% 54.05%
## 329: 20.00% 63.53% 31.14% 7.97% 25.63% 54.05%
## 330: 20.04% 63.53% 31.14% 7.97% 25.78% 54.05%
## 331: 19.77% 61.18% 30.93% 7.65% 25.78% 54.05%
## 332: 19.81% 61.18% 30.93% 7.65% 25.93% 54.05%
## 333: 19.81% 62.35% 31.14% 7.65% 25.63% 54.05%
## 334: 19.81% 62.35% 31.14% 7.81% 25.34% 54.05%
## 335: 19.73% 62.35% 30.93% 7.65% 25.48% 54.05%
## 336: 19.96% 62.35% 31.57% 7.65% 25.78% 55.41%
## 337: 19.96% 62.35% 31.36% 7.89% 25.48% 55.41%
## 338: 19.92% 62.35% 31.36% 7.81% 25.48% 55.41%
## 339: 19.96% 62.35% 31.14% 7.97% 25.48% 55.41%
## 340: 19.88% 62.35% 30.93% 7.97% 25.48% 54.05%
## 341: 19.77% 61.18% 30.93% 7.81% 25.34% 55.41%
## 342: 20.00% 62.35% 31.14% 8.04% 25.48% 55.41%
## 343: 19.88% 61.18% 30.93% 7.97% 25.48% 55.41%
## 344: 19.88% 61.18% 30.93% 7.89% 25.48% 56.76%
## 345: 19.96% 62.35% 30.93% 7.89% 25.63% 56.76%
## 346: 19.96% 62.35% 31.14% 7.73% 25.78% 56.76%
## 347: 20.00% 61.18% 31.14% 7.81% 25.93% 56.76%
## 348: 19.84% 60.00% 30.93% 7.81% 25.78% 55.41%
## 349: 19.88% 60.00% 31.36% 7.73% 25.78% 55.41%
## 350: 19.84% 60.00% 31.36% 7.73% 25.63% 55.41%
## 351: 19.77% 61.18% 30.93% 7.57% 25.78% 55.41%
## 352: 19.77% 60.00% 31.14% 7.57% 25.78% 55.41%
## 353: 19.69% 61.18% 30.93% 7.65% 25.34% 55.41%
## 354: 19.57% 60.00% 31.14% 7.26% 25.63% 55.41%
## 355: 19.53% 60.00% 30.72% 7.41% 25.48% 55.41%
## 356: 19.77% 61.18% 30.72% 7.65% 25.78% 55.41%
## 357: 19.69% 61.18% 31.14% 7.33% 25.78% 55.41%
## 358: 19.65% 61.18% 30.93% 7.41% 25.63% 55.41%
## 359: 19.77% 61.18% 30.93% 7.57% 25.78% 55.41%
## 360: 19.88% 62.35% 31.36% 7.65% 25.63% 55.41%
## 361: 19.69% 61.18% 31.14% 7.33% 25.78% 55.41%
## 362: 19.73% 61.18% 31.14% 7.41% 25.78% 55.41%
## 363: 19.57% 61.18% 30.72% 7.49% 25.34% 55.41%
## 364: 19.57% 61.18% 30.72% 7.57% 25.19% 55.41%
## 365: 19.61% 61.18% 30.72% 7.57% 25.34% 55.41%
## 366: 19.69% 61.18% 30.93% 7.49% 25.63% 55.41%
## 367: 19.65% 62.35% 30.51% 7.49% 25.63% 55.41%
## 368: 19.77% 62.35% 31.36% 7.49% 25.48% 55.41%
## 369: 19.73% 61.18% 30.93% 7.57% 25.63% 55.41%
## 370: 19.61% 62.35% 30.93% 7.33% 25.48% 55.41%
## 371: 19.49% 62.35% 30.93% 7.18% 25.34% 55.41%
## 372: 19.53% 62.35% 30.93% 7.26% 25.34% 55.41%
## 373: 19.46% 61.18% 30.72% 7.26% 25.34% 55.41%
## 374: 19.46% 62.35% 30.51% 7.18% 25.48% 55.41%
## 375: 19.46% 62.35% 30.30% 7.33% 25.34% 55.41%
## 376: 19.46% 61.18% 30.72% 7.33% 25.19% 55.41%
## 377: 19.53% 62.35% 30.72% 7.41% 25.19% 55.41%
## 378: 19.46% 62.35% 30.30% 7.41% 25.19% 55.41%
## 379: 19.42% 62.35% 30.51% 7.33% 25.04% 55.41%
## 380: 19.30% 61.18% 30.30% 7.33% 24.89% 55.41%
## 381: 19.46% 61.18% 30.72% 7.33% 25.19% 55.41%
## 382: 19.57% 60.00% 31.14% 7.41% 25.34% 55.41%
## 383: 19.46% 60.00% 30.93% 7.33% 25.19% 55.41%
## 384: 19.46% 60.00% 31.14% 7.26% 25.19% 55.41%
## 385: 19.49% 60.00% 30.72% 7.57% 25.19% 54.05%
## 386: 19.46% 60.00% 30.93% 7.41% 25.19% 54.05%
## 387: 19.46% 60.00% 30.93% 7.49% 25.04% 54.05%
## 388: 19.42% 60.00% 30.72% 7.49% 25.04% 54.05%
## 389: 19.49% 60.00% 31.14% 7.57% 24.89% 54.05%
## 390: 19.61% 60.00% 31.14% 7.57% 25.19% 55.41%
## 391: 19.61% 60.00% 30.93% 7.65% 25.19% 55.41%
## 392: 19.65% 60.00% 30.93% 7.73% 25.19% 55.41%
## 393: 19.61% 60.00% 31.14% 7.57% 25.19% 55.41%
## 394: 19.57% 60.00% 31.14% 7.49% 25.19% 55.41%
## 395: 19.46% 61.18% 30.72% 7.41% 25.04% 55.41%
## 396: 19.49% 60.00% 30.30% 7.49% 25.48% 55.41%
## 397: 19.53% 61.18% 30.51% 7.57% 25.19% 55.41%
## 398: 19.38% 61.18% 30.08% 7.41% 25.19% 55.41%
## 399: 19.42% 61.18% 30.08% 7.33% 25.48% 55.41%
## 400: 19.53% 62.35% 30.08% 7.41% 25.63% 55.41%
## 401: 19.57% 61.18% 30.51% 7.41% 25.63% 55.41%
## 402: 19.65% 61.18% 30.51% 7.65% 25.48% 55.41%
## 403: 19.57% 61.18% 30.72% 7.33% 25.63% 55.41%
## 404: 19.65% 61.18% 30.93% 7.49% 25.48% 55.41%
## 405: 19.49% 61.18% 30.51% 7.49% 25.19% 55.41%
## 406: 19.69% 61.18% 30.93% 7.65% 25.34% 55.41%
## 407: 19.61% 61.18% 30.30% 7.57% 25.63% 55.41%
## 408: 19.46% 61.18% 30.72% 7.33% 25.19% 55.41%
## 409: 19.53% 61.18% 30.51% 7.41% 25.48% 55.41%
## 410: 19.38% 61.18% 30.30% 7.41% 25.04% 55.41%
## 411: 19.46% 62.35% 30.72% 7.41% 24.89% 55.41%
## 412: 19.61% 62.35% 30.51% 7.41% 25.63% 55.41%
## 413: 19.65% 62.35% 30.51% 7.57% 25.48% 55.41%
## 414: 19.46% 62.35% 30.08% 7.33% 25.48% 55.41%
## 415: 19.46% 62.35% 30.08% 7.33% 25.48% 55.41%
## 416: 19.42% 62.35% 30.08% 7.41% 25.19% 55.41%
## 417: 19.61% 62.35% 30.72% 7.49% 25.34% 55.41%
## 418: 19.42% 62.35% 29.66% 7.41% 25.48% 55.41%
## 419: 19.38% 62.35% 29.87% 7.41% 25.19% 55.41%
## 420: 19.46% 62.35% 30.30% 7.33% 25.34% 55.41%
## 421: 19.42% 62.35% 30.08% 7.41% 25.19% 55.41%
## 422: 19.49% 62.35% 30.30% 7.49% 25.19% 55.41%
## 423: 19.34% 61.18% 30.08% 7.41% 25.04% 55.41%
## 424: 19.38% 62.35% 29.87% 7.41% 25.19% 55.41%
## 425: 19.38% 61.18% 30.08% 7.41% 25.19% 55.41%
## 426: 19.38% 62.35% 29.87% 7.41% 25.19% 55.41%
## 427: 19.38% 61.18% 29.87% 7.57% 25.04% 55.41%
## 428: 19.42% 62.35% 29.87% 7.49% 25.19% 55.41%
## 429: 19.26% 62.35% 29.66% 7.26% 25.19% 55.41%
## 430: 19.26% 62.35% 29.45% 7.33% 25.19% 55.41%
## 431: 19.30% 62.35% 29.87% 7.26% 25.19% 55.41%
## 432: 19.30% 62.35% 29.87% 7.26% 25.19% 55.41%
## 433: 19.18% 62.35% 29.66% 7.18% 25.19% 54.05%
## 434: 19.26% 62.35% 30.08% 7.18% 25.04% 55.41%
## 435: 19.26% 62.35% 30.08% 7.18% 25.04% 55.41%
## 436: 19.22% 62.35% 30.08% 7.10% 25.04% 55.41%
## 437: 19.26% 62.35% 29.45% 7.26% 25.34% 55.41%
## 438: 19.34% 62.35% 29.87% 7.26% 25.34% 55.41%
## 439: 19.38% 61.18% 30.08% 7.33% 25.34% 55.41%
## 440: 19.34% 62.35% 29.87% 7.26% 25.34% 55.41%
## 441: 19.26% 61.18% 29.66% 7.26% 25.34% 55.41%
## 442: 19.38% 62.35% 30.08% 7.33% 25.19% 55.41%
## 443: 19.26% 61.18% 29.87% 7.26% 25.19% 55.41%
## 444: 19.34% 62.35% 29.87% 7.26% 25.34% 55.41%
## 445: 19.30% 62.35% 29.45% 7.33% 25.34% 55.41%
## 446: 19.38% 62.35% 29.87% 7.26% 25.48% 55.41%
## 447: 19.42% 62.35% 30.08% 7.33% 25.34% 55.41%
## 448: 19.30% 62.35% 29.87% 7.33% 25.04% 55.41%
## 449: 19.34% 62.35% 29.87% 7.33% 25.19% 55.41%
## 450: 19.38% 62.35% 29.45% 7.33% 25.63% 55.41%
## 451: 19.46% 63.53% 29.66% 7.33% 25.63% 55.41%
## 452: 19.57% 62.35% 30.08% 7.41% 25.78% 55.41%
## 453: 19.42% 62.35% 30.08% 7.18% 25.63% 55.41%
## 454: 19.46% 62.35% 30.51% 7.18% 25.48% 55.41%
## 455: 19.46% 61.18% 30.72% 7.26% 25.34% 55.41%
## 456: 19.42% 61.18% 30.08% 7.26% 25.63% 55.41%
## 457: 19.38% 61.18% 29.87% 7.26% 25.63% 55.41%
## 458: 19.38% 62.35% 29.87% 7.33% 25.34% 55.41%
## 459: 19.42% 61.18% 30.08% 7.41% 25.34% 55.41%
## 460: 19.38% 62.35% 30.30% 7.18% 25.34% 55.41%
## 461: 19.42% 62.35% 30.30% 7.33% 25.19% 55.41%
## 462: 19.46% 61.18% 30.30% 7.41% 25.34% 55.41%
## 463: 19.53% 61.18% 30.51% 7.41% 25.48% 55.41%
## 464: 19.46% 61.18% 30.72% 7.26% 25.34% 55.41%
## 465: 19.49% 61.18% 30.72% 7.26% 25.48% 55.41%
## 466: 19.42% 61.18% 30.72% 7.10% 25.48% 55.41%
## 467: 19.46% 61.18% 30.51% 7.18% 25.63% 55.41%
## 468: 19.46% 60.00% 30.72% 7.18% 25.63% 55.41%
## 469: 19.61% 61.18% 30.93% 7.26% 25.78% 55.41%
## 470: 19.46% 61.18% 30.51% 7.26% 25.48% 55.41%
## 471: 19.53% 61.18% 30.93% 7.18% 25.63% 55.41%
## 472: 19.49% 61.18% 30.72% 7.26% 25.48% 55.41%
## 473: 19.57% 62.35% 30.72% 7.33% 25.48% 55.41%
## 474: 19.53% 61.18% 30.51% 7.33% 25.63% 55.41%
## 475: 19.38% 61.18% 30.08% 7.18% 25.63% 55.41%
## 476: 19.42% 61.18% 30.30% 7.18% 25.63% 55.41%
## 477: 19.57% 61.18% 30.51% 7.41% 25.63% 55.41%
## 478: 19.46% 60.00% 30.30% 7.33% 25.63% 55.41%
## 479: 19.57% 63.53% 30.08% 7.41% 25.63% 55.41%
## 480: 19.49% 61.18% 30.51% 7.26% 25.63% 55.41%
## 481: 19.57% 62.35% 30.30% 7.49% 25.48% 55.41%
## 482: 19.57% 63.53% 30.72% 7.18% 25.63% 55.41%
## 483: 19.69% 62.35% 30.72% 7.41% 25.78% 55.41%
## 484: 19.65% 62.35% 30.51% 7.33% 25.93% 55.41%
## 485: 19.42% 62.35% 30.30% 7.18% 25.48% 55.41%
## 486: 19.46% 62.35% 30.30% 7.18% 25.63% 55.41%
## 487: 19.49% 60.00% 30.72% 7.18% 25.78% 55.41%
## 488: 19.73% 61.18% 30.93% 7.41% 25.93% 55.41%
## 489: 19.61% 62.35% 30.72% 7.18% 25.93% 55.41%
## 490: 19.81% 61.18% 31.14% 7.41% 26.08% 55.41%
## 491: 19.73% 61.18% 31.14% 7.33% 25.93% 55.41%
## 492: 19.69% 62.35% 30.93% 7.33% 25.78% 55.41%
## 493: 19.61% 62.35% 30.30% 7.33% 25.93% 55.41%
## 494: 19.69% 62.35% 30.51% 7.41% 25.93% 55.41%
## 495: 19.65% 63.53% 30.51% 7.33% 25.78% 55.41%
## 496: 19.57% 62.35% 30.30% 7.26% 25.93% 55.41%
## 497: 19.53% 62.35% 30.30% 7.26% 25.78% 55.41%
## 498: 19.69% 63.53% 30.72% 7.33% 25.78% 55.41%
## 499: 19.77% 63.53% 30.72% 7.41% 25.93% 55.41%
## 500: 19.81% 63.53% 30.72% 7.41% 26.08% 55.41%
## 501: 19.77% 62.35% 30.93% 7.26% 26.23% 55.41%
## 502: 19.69% 62.35% 30.72% 7.33% 25.93% 55.41%
## 503: 19.73% 62.35% 30.93% 7.26% 26.08% 55.41%
## 504: 19.73% 62.35% 30.72% 7.33% 26.08% 55.41%
## 505: 19.65% 61.18% 30.93% 7.41% 25.63% 55.41%
## 506: 19.81% 63.53% 30.93% 7.49% 25.78% 55.41%
## 507: 19.88% 63.53% 30.93% 7.65% 25.78% 55.41%
## 508: 19.69% 62.35% 30.93% 7.49% 25.48% 55.41%
## 509: 19.69% 61.18% 31.14% 7.49% 25.48% 55.41%
## 510: 19.69% 60.00% 31.14% 7.49% 25.63% 55.41%
## 511: 19.69% 61.18% 31.14% 7.49% 25.48% 55.41%
## 512: 19.61% 61.18% 30.72% 7.49% 25.48% 55.41%
## 513: 19.73% 61.18% 30.72% 7.65% 25.63% 55.41%
## 514: 19.69% 61.18% 31.14% 7.49% 25.48% 55.41%
## 515: 19.65% 61.18% 30.72% 7.57% 25.48% 55.41%
## 516: 19.57% 61.18% 30.72% 7.26% 25.78% 55.41%
## 517: 19.69% 61.18% 30.93% 7.33% 25.93% 55.41%
## 518: 19.69% 61.18% 31.14% 7.26% 25.93% 55.41%
## 519: 19.77% 61.18% 30.93% 7.49% 25.93% 55.41%
## 520: 19.81% 61.18% 31.14% 7.57% 25.78% 55.41%
## 521: 19.84% 61.18% 30.93% 7.73% 25.78% 55.41%
## 522: 19.81% 61.18% 30.93% 7.57% 25.93% 55.41%
## 523: 19.77% 61.18% 30.93% 7.49% 25.93% 55.41%
## 524: 19.77% 61.18% 30.93% 7.49% 25.93% 55.41%
## 525: 19.77% 61.18% 30.93% 7.57% 25.78% 55.41%
## 526: 19.77% 61.18% 30.93% 7.65% 25.63% 55.41%
## 527: 19.88% 61.18% 30.93% 7.73% 25.93% 55.41%
## 528: 19.88% 61.18% 30.93% 7.73% 25.93% 55.41%
## 529: 19.84% 61.18% 30.93% 7.73% 25.78% 55.41%
## 530: 20.00% 61.18% 31.14% 7.81% 26.08% 55.41%
## 531: 19.88% 61.18% 30.93% 7.65% 26.08% 55.41%
## 532: 19.92% 61.18% 30.93% 7.81% 25.93% 55.41%
## 533: 19.88% 61.18% 31.14% 7.65% 25.93% 55.41%
## 534: 19.88% 61.18% 31.14% 7.57% 26.08% 55.41%
## 535: 19.84% 61.18% 31.14% 7.57% 25.93% 55.41%
## 536: 19.81% 61.18% 30.93% 7.49% 25.93% 56.76%
## 537: 19.81% 61.18% 30.72% 7.49% 26.08% 56.76%
## 538: 19.84% 61.18% 30.72% 7.57% 26.08% 56.76%
## 539: 19.73% 61.18% 30.30% 7.41% 26.23% 56.76%
## 540: 19.77% 61.18% 30.30% 7.57% 26.08% 56.76%
## 541: 19.81% 61.18% 30.30% 7.57% 26.23% 56.76%
## 542: 19.73% 61.18% 30.30% 7.49% 26.08% 56.76%
## 543: 19.77% 61.18% 30.51% 7.41% 26.23% 56.76%
## 544: 19.84% 61.18% 30.72% 7.57% 26.08% 56.76%
## 545: 19.69% 61.18% 30.30% 7.49% 25.93% 56.76%
## 546: 19.77% 61.18% 30.51% 7.49% 26.08% 56.76%
## 547: 19.81% 61.18% 30.93% 7.41% 26.08% 56.76%
## 548: 19.84% 61.18% 31.36% 7.33% 26.08% 56.76%
## 549: 19.77% 61.18% 31.14% 7.26% 26.08% 56.76%
## 550: 19.73% 61.18% 30.72% 7.33% 26.08% 56.76%
## 551: 19.77% 61.18% 31.14% 7.26% 26.08% 56.76%
## 552: 19.88% 61.18% 30.93% 7.57% 26.08% 56.76%
## 553: 19.88% 61.18% 31.14% 7.41% 26.23% 56.76%
## 554: 19.84% 61.18% 31.14% 7.41% 26.23% 55.41%
## 555: 19.77% 61.18% 30.93% 7.41% 25.93% 56.76%
## 556: 19.88% 61.18% 31.14% 7.41% 26.23% 56.76%
## 557: 19.81% 61.18% 30.93% 7.41% 26.08% 56.76%
## 558: 19.73% 61.18% 31.14% 7.18% 26.08% 56.76%
## 559: 19.84% 61.18% 31.36% 7.33% 26.08% 56.76%
## 560: 19.81% 61.18% 31.36% 7.26% 26.08% 56.76%
## 561: 19.77% 60.00% 31.36% 7.26% 26.08% 56.76%
## 562: 19.84% 62.35% 31.36% 7.18% 26.23% 56.76%
## 563: 19.77% 60.00% 31.14% 7.18% 26.38% 56.76%
## 564: 19.73% 60.00% 31.36% 7.26% 25.93% 56.76%
## 565: 19.69% 60.00% 31.36% 7.10% 26.08% 56.76%
## 566: 19.61% 60.00% 31.14% 7.10% 25.93% 56.76%
## 567: 19.69% 61.18% 31.14% 7.18% 25.93% 56.76%
## 568: 19.84% 61.18% 31.14% 7.33% 26.23% 56.76%
## 569: 19.88% 61.18% 31.36% 7.33% 26.23% 56.76%
## 570: 19.88% 60.00% 31.36% 7.33% 26.38% 56.76%
## 571: 19.84% 60.00% 31.14% 7.33% 26.38% 56.76%
## 572: 19.77% 60.00% 30.51% 7.33% 26.53% 56.76%
## 573: 19.69% 60.00% 30.30% 7.33% 26.38% 56.76%
## 574: 19.61% 60.00% 30.72% 7.10% 26.23% 56.76%
## 575: 19.69% 60.00% 30.93% 7.10% 26.38% 56.76%
## 576: 19.65% 60.00% 30.72% 7.10% 26.38% 56.76%
## 577: 19.73% 60.00% 30.93% 7.18% 26.38% 56.76%
## 578: 19.84% 60.00% 31.14% 7.33% 26.38% 56.76%
## 579: 19.61% 60.00% 30.72% 7.18% 26.08% 56.76%
## 580: 19.73% 60.00% 30.93% 7.26% 26.23% 56.76%
## 581: 19.57% 60.00% 30.51% 7.18% 26.08% 56.76%
## 582: 19.69% 60.00% 30.93% 7.18% 26.23% 56.76%
## 583: 19.81% 60.00% 30.72% 7.33% 26.53% 56.76%
## 584: 19.81% 60.00% 30.51% 7.41% 26.53% 56.76%
## 585: 19.81% 60.00% 30.72% 7.33% 26.53% 56.76%
## 586: 19.81% 60.00% 30.72% 7.33% 26.53% 56.76%
## 587: 19.77% 60.00% 30.72% 7.33% 26.38% 56.76%
## 588: 19.77% 60.00% 30.93% 7.26% 26.38% 56.76%
## 589: 19.81% 60.00% 30.93% 7.33% 26.38% 56.76%
## 590: 19.77% 60.00% 30.93% 7.26% 26.38% 56.76%
## 591: 19.84% 60.00% 30.93% 7.41% 26.38% 56.76%
## 592: 19.84% 60.00% 31.14% 7.26% 26.53% 56.76%
## 593: 19.84% 60.00% 30.93% 7.33% 26.53% 56.76%
## 594: 19.81% 60.00% 31.14% 7.26% 26.38% 56.76%
## 595: 19.81% 60.00% 31.14% 7.26% 26.38% 56.76%
## 596: 19.84% 60.00% 31.14% 7.26% 26.53% 56.76%
## 597: 19.84% 60.00% 31.14% 7.26% 26.53% 56.76%
## 598: 19.88% 60.00% 31.36% 7.26% 26.53% 56.76%
## 599: 19.96% 60.00% 31.78% 7.26% 26.53% 56.76%
## 600: 20.00% 60.00% 31.78% 7.33% 26.53% 56.76%
## 601: 20.00% 60.00% 31.78% 7.41% 26.38% 56.76%
## 602: 20.00% 60.00% 31.57% 7.49% 26.38% 56.76%
## 603: 19.81% 60.00% 31.57% 7.33% 25.93% 56.76%
## 604: 19.84% 60.00% 31.57% 7.33% 26.08% 56.76%
## 605: 19.84% 60.00% 31.57% 7.33% 26.08% 56.76%
## 606: 19.84% 60.00% 31.57% 7.33% 26.08% 56.76%
## 607: 19.92% 60.00% 31.57% 7.33% 26.38% 56.76%
## 608: 19.96% 60.00% 31.78% 7.33% 26.38% 56.76%
## 609: 19.96% 61.18% 31.57% 7.41% 26.23% 56.76%
## 610: 20.00% 60.00% 31.57% 7.49% 26.38% 56.76%
## 611: 20.00% 60.00% 31.78% 7.49% 26.23% 56.76%
## 612: 19.96% 60.00% 31.57% 7.41% 26.38% 56.76%
## 613: 20.04% 60.00% 31.57% 7.49% 26.53% 56.76%
## 614: 19.96% 60.00% 31.57% 7.41% 26.38% 56.76%
## 615: 19.84% 58.82% 31.36% 7.33% 26.38% 56.76%
## 616: 19.81% 60.00% 31.14% 7.26% 26.38% 56.76%
## 617: 19.81% 60.00% 31.57% 7.26% 26.08% 56.76%
## 618: 19.73% 60.00% 31.57% 7.18% 25.93% 56.76%
## 619: 19.81% 61.18% 31.57% 7.18% 26.08% 56.76%
## 620: 19.77% 62.35% 31.14% 7.26% 25.93% 56.76%
## 621: 19.73% 62.35% 31.14% 7.10% 26.08% 56.76%
## 622: 19.73% 62.35% 30.72% 7.26% 26.08% 56.76%
## 623: 19.73% 62.35% 31.36% 7.10% 25.93% 56.76%
## 624: 19.77% 63.53% 31.36% 7.10% 25.93% 56.76%
## 625: 19.92% 63.53% 31.14% 7.18% 26.53% 56.76%
## 626: 19.73% 62.35% 31.14% 7.02% 26.23% 56.76%
## 627: 19.84% 62.35% 31.36% 7.10% 26.38% 56.76%
## 628: 19.77% 62.35% 31.36% 7.10% 26.08% 56.76%
## 629: 19.69% 62.35% 30.93% 7.18% 25.93% 56.76%
## 630: 19.65% 61.18% 30.93% 7.10% 26.08% 56.76%
## 631: 19.65% 61.18% 31.36% 7.02% 25.93% 56.76%
## 632: 19.65% 61.18% 31.36% 7.02% 25.93% 56.76%
## 633: 19.65% 62.35% 31.14% 7.02% 25.93% 56.76%
## 634: 19.53% 62.35% 31.14% 7.02% 25.48% 56.76%
## 635: 19.53% 62.35% 31.14% 7.10% 25.34% 56.76%
## 636: 19.53% 62.35% 31.14% 7.02% 25.48% 56.76%
## 637: 19.77% 62.35% 30.93% 7.18% 26.23% 56.76%
## 638: 19.57% 62.35% 31.14% 7.10% 25.48% 56.76%
## 639: 19.69% 61.18% 31.36% 7.18% 25.78% 56.76%
## 640: 19.42% 62.35% 30.72% 7.02% 25.34% 56.76%
## 641: 19.49% 62.35% 31.14% 6.94% 25.48% 56.76%
## 642: 19.65% 62.35% 31.36% 7.02% 25.78% 56.76%
## 643: 19.61% 62.35% 31.36% 7.10% 25.48% 56.76%
## 644: 19.77% 62.35% 31.36% 7.18% 25.93% 56.76%
## 645: 19.77% 62.35% 31.36% 7.10% 26.08% 56.76%
## 646: 19.69% 62.35% 30.93% 7.10% 26.08% 56.76%
## 647: 19.61% 61.18% 31.14% 7.10% 25.78% 56.76%
## 648: 19.53% 61.18% 30.93% 7.10% 25.63% 56.76%
## 649: 19.42% 60.00% 30.72% 7.02% 25.63% 56.76%
## 650: 19.49% 61.18% 30.72% 7.10% 25.63% 56.76%
## 651: 19.61% 60.00% 30.93% 7.10% 26.08% 56.76%
## 652: 19.61% 60.00% 31.14% 7.02% 26.08% 56.76%
## 653: 19.53% 60.00% 31.14% 7.10% 25.63% 56.76%
## 654: 19.53% 60.00% 30.93% 7.10% 25.78% 56.76%
## 655: 19.46% 60.00% 30.93% 7.10% 25.48% 56.76%
## 656: 19.46% 60.00% 30.93% 7.10% 25.48% 56.76%
## 657: 19.42% 60.00% 31.14% 7.02% 25.34% 56.76%
## 658: 19.42% 60.00% 31.14% 6.94% 25.48% 56.76%
## 659: 19.61% 60.00% 31.14% 7.10% 25.93% 56.76%
## 660: 19.53% 60.00% 31.14% 7.10% 25.78% 55.41%
## 661: 19.61% 60.00% 31.14% 7.10% 25.93% 56.76%
## 662: 19.61% 60.00% 31.36% 7.10% 25.78% 56.76%
## 663: 19.53% 60.00% 30.93% 7.10% 25.78% 56.76%
## 664: 19.49% 60.00% 30.93% 7.02% 25.78% 56.76%
## 665: 19.53% 60.00% 31.14% 7.02% 25.78% 56.76%
## 666: 19.57% 60.00% 31.14% 7.02% 25.93% 56.76%
## 667: 19.53% 61.18% 31.14% 6.94% 25.78% 56.76%
## 668: 19.53% 61.18% 31.14% 6.94% 25.78% 56.76%
## 669: 19.53% 61.18% 31.14% 7.02% 25.78% 55.41%
## 670: 19.57% 62.35% 31.14% 7.02% 25.78% 55.41%
## 671: 19.57% 61.18% 30.93% 6.94% 26.08% 56.76%
## 672: 19.73% 61.18% 31.14% 7.18% 26.08% 56.76%
## 673: 19.77% 62.35% 31.36% 7.18% 25.93% 56.76%
## 674: 19.61% 62.35% 31.36% 7.02% 25.63% 56.76%
## 675: 19.49% 61.18% 31.14% 7.02% 25.63% 55.41%
## 676: 19.61% 61.18% 31.14% 7.10% 25.93% 55.41%
## 677: 19.69% 61.18% 30.93% 7.18% 26.23% 55.41%
## 678: 19.77% 61.18% 31.14% 7.18% 26.38% 55.41%
## 679: 19.69% 61.18% 30.93% 7.26% 26.08% 55.41%
## 680: 19.57% 61.18% 30.93% 7.18% 25.78% 55.41%
## 681: 19.61% 60.00% 31.36% 7.18% 25.78% 55.41%
## 682: 19.65% 60.00% 31.14% 7.18% 26.08% 55.41%
## 683: 19.65% 60.00% 31.57% 7.18% 25.63% 56.76%
## 684: 19.65% 60.00% 31.36% 7.10% 25.93% 56.76%
## 685: 19.57% 60.00% 31.36% 7.18% 25.63% 55.41%
## 686: 19.61% 60.00% 31.36% 7.18% 25.78% 55.41%
## 687: 19.53% 61.18% 30.72% 7.18% 25.78% 55.41%
## 688: 19.57% 61.18% 30.93% 7.18% 25.78% 55.41%
## 689: 19.65% 61.18% 31.14% 7.18% 25.93% 55.41%
## 690: 19.69% 61.18% 31.36% 7.18% 25.93% 55.41%
## 691: 19.65% 60.00% 30.93% 7.26% 26.08% 55.41%
## 692: 19.61% 61.18% 30.93% 7.02% 26.08% 56.76%
## 693: 19.57% 61.18% 30.72% 7.02% 26.08% 56.76%
## 694: 19.77% 62.35% 31.14% 7.18% 26.08% 56.76%
## 695: 19.61% 60.00% 30.93% 7.18% 25.93% 56.76%
## 696: 19.73% 61.18% 31.14% 7.18% 26.08% 56.76%
## 697: 19.65% 61.18% 30.93% 7.10% 26.08% 56.76%
## 698: 19.69% 60.00% 31.14% 7.18% 26.08% 56.76%
## 699: 19.73% 61.18% 31.14% 7.18% 26.08% 56.76%
## 700: 19.88% 62.35% 31.36% 7.26% 26.23% 56.76%
## 701: 19.81% 61.18% 31.36% 7.18% 26.23% 56.76%
## 702: 19.81% 61.18% 31.14% 7.33% 26.08% 56.76%
## 703: 19.96% 62.35% 31.57% 7.33% 26.23% 56.76%
## 704: 19.88% 62.35% 31.14% 7.33% 26.23% 56.76%
## 705: 19.77% 62.35% 30.72% 7.33% 26.08% 56.76%
## 706: 19.81% 62.35% 31.14% 7.33% 25.93% 56.76%
## 707: 19.73% 62.35% 30.93% 7.26% 26.08% 55.41%
## 708: 19.69% 61.18% 30.93% 7.26% 25.93% 56.76%
## 709: 19.73% 62.35% 30.93% 7.26% 25.93% 56.76%
## 710: 19.69% 62.35% 30.93% 7.18% 26.08% 55.41%
## 711: 19.65% 62.35% 30.93% 7.18% 25.93% 55.41%
## 712: 19.69% 61.18% 30.93% 7.18% 26.08% 56.76%
## 713: 19.77% 62.35% 31.14% 7.18% 26.08% 56.76%
## 714: 19.69% 61.18% 30.93% 7.33% 25.93% 55.41%
## 715: 19.77% 61.18% 31.14% 7.33% 25.93% 56.76%
## 716: 19.92% 62.35% 31.14% 7.41% 26.23% 56.76%
## 717: 19.77% 61.18% 31.14% 7.41% 25.93% 55.41%
## 718: 19.73% 61.18% 31.14% 7.33% 25.93% 55.41%
## 719: 19.77% 61.18% 31.57% 7.26% 25.93% 55.41%
## 720: 19.65% 61.18% 31.14% 7.33% 25.63% 55.41%
## 721: 19.57% 61.18% 30.93% 7.26% 25.63% 55.41%
## 722: 19.69% 61.18% 31.14% 7.33% 25.78% 55.41%
## 723: 19.61% 61.18% 30.93% 7.26% 25.78% 55.41%
## 724: 19.61% 61.18% 31.14% 7.18% 25.78% 55.41%
## 725: 19.65% 61.18% 30.72% 7.26% 26.08% 55.41%
## 726: 19.42% 61.18% 30.30% 7.18% 25.63% 55.41%
## 727: 19.46% 61.18% 30.30% 7.10% 25.93% 55.41%
## 728: 19.57% 61.18% 30.93% 7.18% 25.78% 55.41%
## 729: 19.61% 61.18% 30.72% 7.26% 25.93% 55.41%
## 730: 19.65% 61.18% 30.72% 7.41% 25.78% 55.41%
## 731: 19.73% 61.18% 30.93% 7.41% 25.93% 55.41%
## 732: 19.65% 61.18% 30.72% 7.33% 25.93% 55.41%
## 733: 19.65% 61.18% 30.72% 7.49% 25.63% 55.41%
## 734: 19.61% 61.18% 30.72% 7.41% 25.63% 55.41%
## 735: 19.69% 61.18% 31.14% 7.33% 25.78% 55.41%
## 736: 19.61% 61.18% 30.93% 7.33% 25.63% 55.41%
## 737: 19.61% 61.18% 30.72% 7.41% 25.63% 55.41%
## 738: 19.53% 61.18% 30.51% 7.33% 25.63% 55.41%
## 739: 19.61% 61.18% 30.72% 7.41% 25.63% 55.41%
## 740: 19.61% 61.18% 30.72% 7.33% 25.78% 55.41%
## 741: 19.65% 61.18% 31.14% 7.33% 25.63% 55.41%
## 742: 19.61% 61.18% 30.72% 7.41% 25.63% 55.41%
## 743: 19.46% 61.18% 30.30% 7.33% 25.48% 55.41%
## 744: 19.49% 61.18% 30.72% 7.26% 25.48% 55.41%
## 745: 19.46% 60.00% 30.51% 7.26% 25.63% 55.41%
## 746: 19.46% 60.00% 30.72% 7.26% 25.48% 55.41%
## 747: 19.53% 61.18% 30.93% 7.26% 25.48% 55.41%
## 748: 19.49% 61.18% 31.14% 7.18% 25.34% 55.41%
## 749: 19.42% 61.18% 30.30% 7.26% 25.48% 55.41%
## 750: 19.42% 61.18% 30.51% 7.26% 25.34% 55.41%
## 751: 19.49% 61.18% 30.93% 7.18% 25.48% 55.41%
## 752: 19.42% 61.18% 30.72% 7.10% 25.48% 55.41%
## 753: 19.46% 61.18% 30.72% 7.26% 25.34% 55.41%
## 754: 19.53% 61.18% 30.93% 7.33% 25.34% 55.41%
## 755: 19.46% 61.18% 30.93% 7.26% 25.19% 55.41%
## 756: 19.46% 61.18% 30.72% 7.26% 25.34% 55.41%
## 757: 19.46% 61.18% 30.93% 7.26% 25.19% 55.41%
## 758: 19.53% 61.18% 30.72% 7.33% 25.48% 55.41%
## 759: 19.42% 61.18% 30.72% 7.26% 25.19% 55.41%
## 760: 19.42% 61.18% 30.72% 7.26% 25.19% 55.41%
## 761: 19.46% 61.18% 30.51% 7.26% 25.48% 55.41%
## 762: 19.34% 61.18% 30.30% 7.26% 25.19% 55.41%
## 763: 19.38% 61.18% 30.30% 7.33% 25.19% 55.41%
## 764: 19.38% 61.18% 30.30% 7.33% 25.19% 55.41%
## 765: 19.38% 61.18% 30.08% 7.33% 25.34% 55.41%
## 766: 19.34% 61.18% 30.30% 7.26% 25.19% 55.41%
## 767: 19.30% 61.18% 30.30% 7.26% 25.04% 55.41%
## 768: 19.30% 61.18% 30.08% 7.26% 25.19% 55.41%
## 769: 19.30% 61.18% 30.30% 7.26% 25.04% 55.41%
## 770: 19.42% 61.18% 30.72% 7.26% 25.19% 55.41%
## 771: 19.30% 61.18% 30.08% 7.26% 25.19% 55.41%
## 772: 19.26% 61.18% 30.08% 7.26% 25.04% 55.41%
## 773: 19.42% 61.18% 30.72% 7.18% 25.34% 55.41%
## 774: 19.26% 61.18% 30.30% 7.18% 25.04% 55.41%
## 775: 19.34% 61.18% 30.51% 7.18% 25.19% 55.41%
## 776: 19.22% 61.18% 30.08% 7.18% 25.04% 55.41%
## 777: 19.22% 61.18% 30.30% 7.18% 24.89% 55.41%
## 778: 19.18% 61.18% 30.08% 7.18% 24.89% 55.41%
## 779: 19.26% 61.18% 30.30% 7.18% 25.04% 55.41%
## 780: 19.22% 61.18% 30.08% 7.18% 25.04% 55.41%
## 781: 19.18% 61.18% 29.87% 7.18% 25.04% 55.41%
## 782: 19.18% 61.18% 29.87% 7.10% 25.19% 55.41%
## 783: 19.30% 61.18% 30.08% 7.18% 25.34% 55.41%
## 784: 19.18% 61.18% 29.87% 7.10% 25.19% 55.41%
## 785: 19.22% 61.18% 30.08% 7.18% 25.04% 55.41%
## 786: 19.22% 61.18% 29.87% 7.18% 25.19% 55.41%
## 787: 19.22% 61.18% 30.08% 7.10% 25.19% 55.41%
## 788: 19.34% 61.18% 30.08% 7.18% 25.48% 55.41%
## 789: 19.26% 61.18% 30.30% 7.02% 25.34% 55.41%
## 790: 19.22% 61.18% 29.87% 7.10% 25.34% 55.41%
## 791: 19.26% 61.18% 30.08% 7.10% 25.34% 55.41%
## 792: 19.26% 61.18% 30.08% 7.02% 25.48% 55.41%
## 793: 19.38% 61.18% 30.51% 7.10% 25.48% 55.41%
## 794: 19.22% 61.18% 29.87% 7.10% 25.34% 55.41%
## 795: 19.26% 61.18% 29.87% 7.18% 25.34% 55.41%
## 796: 19.30% 61.18% 29.87% 7.18% 25.48% 55.41%
## 797: 19.38% 61.18% 30.30% 7.18% 25.48% 55.41%
## 798: 19.30% 61.18% 29.87% 7.18% 25.48% 55.41%
## 799: 19.34% 61.18% 30.30% 7.02% 25.63% 55.41%
## 800: 19.26% 61.18% 30.30% 7.02% 25.34% 55.41%
## 801: 19.42% 61.18% 30.30% 7.18% 25.63% 55.41%
## 802: 19.38% 61.18% 30.30% 7.18% 25.48% 55.41%
## 803: 19.30% 61.18% 29.87% 7.18% 25.48% 55.41%
## 804: 19.34% 61.18% 29.87% 7.18% 25.63% 55.41%
## 805: 19.38% 61.18% 30.30% 7.10% 25.63% 55.41%
## 806: 19.38% 61.18% 30.08% 7.18% 25.63% 55.41%
## 807: 19.38% 61.18% 30.08% 7.18% 25.63% 55.41%
## 808: 19.38% 61.18% 30.08% 7.26% 25.48% 55.41%
## 809: 19.42% 61.18% 30.08% 7.26% 25.63% 55.41%
## 810: 19.38% 61.18% 30.08% 7.26% 25.48% 55.41%
## 811: 19.26% 61.18% 30.08% 7.10% 25.34% 55.41%
## 812: 19.30% 61.18% 30.08% 7.18% 25.34% 55.41%
## 813: 19.34% 61.18% 30.30% 7.18% 25.34% 55.41%
## 814: 19.30% 61.18% 30.08% 7.18% 25.34% 55.41%
## 815: 19.30% 61.18% 30.08% 7.18% 25.34% 55.41%
## 816: 19.34% 61.18% 30.08% 7.33% 25.19% 55.41%
## 817: 19.38% 61.18% 30.30% 7.26% 25.34% 55.41%
## 818: 19.49% 61.18% 30.51% 7.33% 25.48% 55.41%
## 819: 19.46% 61.18% 30.51% 7.26% 25.48% 55.41%
## 820: 19.46% 61.18% 30.30% 7.33% 25.48% 55.41%
## 821: 19.42% 61.18% 30.30% 7.26% 25.48% 55.41%
## 822: 19.42% 61.18% 30.30% 7.33% 25.34% 55.41%
## 823: 19.46% 61.18% 30.08% 7.33% 25.63% 55.41%
## 824: 19.49% 61.18% 30.30% 7.26% 25.78% 55.41%
## 825: 19.38% 61.18% 30.08% 7.18% 25.63% 55.41%
## 826: 19.38% 61.18% 30.08% 7.18% 25.63% 55.41%
## 827: 19.38% 61.18% 30.30% 7.18% 25.48% 55.41%
## 828: 19.42% 61.18% 30.08% 7.26% 25.63% 55.41%
## 829: 19.42% 61.18% 30.08% 7.18% 25.78% 55.41%
## 830: 19.49% 61.18% 30.08% 7.33% 25.78% 55.41%
## 831: 19.46% 61.18% 30.08% 7.26% 25.78% 55.41%
## 832: 19.46% 61.18% 30.30% 7.18% 25.78% 55.41%
## 833: 19.46% 61.18% 29.87% 7.41% 25.63% 55.41%
## 834: 19.46% 61.18% 30.30% 7.26% 25.63% 55.41%
## 835: 19.46% 61.18% 30.08% 7.33% 25.63% 55.41%
## 836: 19.42% 61.18% 29.87% 7.33% 25.63% 55.41%
## 837: 19.49% 61.18% 30.08% 7.26% 25.93% 55.41%
## 838: 19.57% 61.18% 29.87% 7.41% 26.08% 55.41%
## 839: 19.53% 61.18% 30.51% 7.18% 25.93% 55.41%
## 840: 19.57% 61.18% 30.51% 7.26% 25.93% 55.41%
## 841: 19.42% 61.18% 29.87% 7.26% 25.78% 55.41%
## 842: 19.53% 61.18% 30.30% 7.33% 25.78% 55.41%
## 843: 19.49% 61.18% 30.08% 7.33% 25.78% 55.41%
## 844: 19.49% 60.00% 30.08% 7.33% 25.93% 55.41%
## 845: 19.49% 60.00% 30.30% 7.41% 25.63% 55.41%
## 846: 19.49% 60.00% 30.08% 7.41% 25.78% 55.41%
## 847: 19.46% 60.00% 30.08% 7.41% 25.63% 55.41%
## 848: 19.49% 60.00% 30.08% 7.49% 25.63% 55.41%
## 849: 19.49% 61.18% 30.30% 7.33% 25.63% 55.41%
## 850: 19.57% 61.18% 30.30% 7.41% 25.78% 55.41%
## 851: 19.53% 61.18% 30.30% 7.33% 25.78% 55.41%
## 852: 19.61% 61.18% 30.51% 7.33% 25.93% 55.41%
## 853: 19.65% 61.18% 30.72% 7.33% 25.93% 55.41%
## 854: 19.61% 61.18% 30.72% 7.33% 25.78% 55.41%
## 855: 19.61% 62.35% 30.72% 7.41% 25.48% 55.41%
## 856: 19.57% 62.35% 30.51% 7.33% 25.63% 55.41%
## 857: 19.61% 62.35% 30.51% 7.41% 25.63% 55.41%
## 858: 19.61% 62.35% 30.51% 7.49% 25.48% 55.41%
## 859: 19.61% 62.35% 30.51% 7.49% 25.48% 55.41%
## 860: 19.57% 62.35% 30.51% 7.41% 25.48% 55.41%
## 861: 19.61% 62.35% 30.72% 7.41% 25.48% 55.41%
## 862: 19.65% 62.35% 30.72% 7.49% 25.48% 55.41%
## 863: 19.65% 62.35% 30.72% 7.49% 25.48% 55.41%
## 864: 19.69% 62.35% 30.72% 7.49% 25.63% 55.41%
## 865: 19.69% 62.35% 30.72% 7.49% 25.63% 55.41%
## 866: 19.73% 62.35% 30.72% 7.57% 25.63% 55.41%
## 867: 19.61% 62.35% 30.51% 7.41% 25.63% 55.41%
## 868: 19.69% 62.35% 30.51% 7.49% 25.78% 55.41%
## 869: 19.61% 62.35% 30.72% 7.33% 25.63% 55.41%
## 870: 19.61% 62.35% 30.51% 7.41% 25.63% 55.41%
## 871: 19.57% 62.35% 30.72% 7.41% 25.34% 55.41%
## 872: 19.65% 62.35% 30.51% 7.33% 25.93% 55.41%
## 873: 19.61% 62.35% 30.51% 7.33% 25.78% 55.41%
## 874: 19.61% 62.35% 30.51% 7.33% 25.78% 55.41%
## 875: 19.65% 62.35% 30.51% 7.41% 25.78% 55.41%
## 876: 19.57% 62.35% 30.51% 7.26% 25.78% 55.41%
## 877: 19.57% 62.35% 30.51% 7.26% 25.78% 55.41%
## 878: 19.53% 62.35% 30.51% 7.26% 25.63% 55.41%
## 879: 19.61% 62.35% 30.51% 7.33% 25.78% 55.41%
## 880: 19.49% 61.18% 30.51% 7.18% 25.78% 55.41%
## 881: 19.57% 62.35% 30.51% 7.26% 25.78% 55.41%
## 882: 19.53% 62.35% 30.51% 7.26% 25.63% 55.41%
## 883: 19.49% 61.18% 30.51% 7.18% 25.78% 55.41%
## 884: 19.49% 62.35% 30.51% 7.18% 25.63% 55.41%
## 885: 19.46% 61.18% 30.51% 7.18% 25.63% 55.41%
## 886: 19.42% 61.18% 30.51% 7.10% 25.63% 55.41%
## 887: 19.46% 61.18% 30.51% 7.18% 25.63% 55.41%
## 888: 19.46% 62.35% 30.51% 7.02% 25.78% 55.41%
## 889: 19.46% 62.35% 30.51% 7.02% 25.78% 55.41%
## 890: 19.46% 62.35% 30.51% 7.02% 25.78% 55.41%
## 891: 19.34% 61.18% 30.51% 6.94% 25.63% 55.41%
## 892: 19.38% 61.18% 30.51% 7.02% 25.63% 55.41%
## 893: 19.46% 61.18% 30.30% 7.10% 25.93% 55.41%
## 894: 19.42% 61.18% 30.51% 7.02% 25.78% 55.41%
## 895: 19.46% 61.18% 30.51% 7.02% 25.93% 55.41%
## 896: 19.53% 61.18% 30.51% 7.18% 25.93% 55.41%
## 897: 19.46% 61.18% 30.51% 7.02% 25.93% 55.41%
## 898: 19.38% 61.18% 30.51% 6.94% 25.78% 55.41%
## 899: 19.49% 61.18% 30.51% 7.18% 25.78% 55.41%
## 900: 19.53% 61.18% 30.51% 7.18% 25.93% 55.41%
## 901: 19.42% 61.18% 30.51% 7.02% 25.78% 55.41%
## 902: 19.49% 61.18% 30.51% 7.18% 25.78% 55.41%
## 903: 19.42% 61.18% 30.51% 7.02% 25.78% 55.41%
## 904: 19.42% 61.18% 30.51% 7.02% 25.78% 55.41%
## 905: 19.42% 61.18% 30.51% 7.02% 25.78% 55.41%
## 906: 19.46% 61.18% 30.51% 7.10% 25.78% 55.41%
## 907: 19.46% 61.18% 30.51% 7.26% 25.63% 54.05%
## 908: 19.46% 61.18% 30.51% 7.18% 25.78% 54.05%
## 909: 19.53% 61.18% 30.51% 7.18% 25.93% 55.41%
## 910: 19.49% 61.18% 30.51% 7.10% 25.93% 55.41%
## 911: 19.46% 61.18% 30.30% 7.10% 25.93% 55.41%
## 912: 19.46% 61.18% 30.30% 7.26% 25.63% 55.41%
## 913: 19.42% 61.18% 30.30% 7.18% 25.78% 54.05%
## 914: 19.38% 61.18% 30.30% 7.10% 25.78% 54.05%
## 915: 19.38% 61.18% 30.30% 7.18% 25.63% 54.05%
## 916: 19.49% 61.18% 30.30% 7.26% 25.78% 55.41%
## 917: 19.30% 61.18% 30.08% 7.10% 25.63% 54.05%
## 918: 19.30% 61.18% 30.30% 7.10% 25.48% 54.05%
## 919: 19.42% 61.18% 30.51% 7.10% 25.78% 54.05%
## 920: 19.42% 62.35% 30.51% 7.10% 25.63% 54.05%
## 921: 19.42% 62.35% 30.30% 7.18% 25.63% 54.05%
## 922: 19.38% 61.18% 30.30% 7.18% 25.63% 54.05%
## 923: 19.30% 61.18% 30.30% 7.10% 25.48% 54.05%
## 924: 19.42% 61.18% 30.51% 7.26% 25.48% 54.05%
## 925: 19.34% 61.18% 30.30% 7.18% 25.48% 54.05%
## 926: 19.26% 61.18% 30.08% 7.18% 25.34% 54.05%
## 927: 19.22% 61.18% 30.08% 7.10% 25.34% 54.05%
## 928: 19.34% 61.18% 30.51% 7.10% 25.48% 54.05%
## 929: 19.34% 61.18% 30.51% 7.18% 25.34% 54.05%
## 930: 19.30% 61.18% 30.30% 7.10% 25.48% 54.05%
## 931: 19.26% 61.18% 30.30% 7.10% 25.34% 54.05%
## 932: 19.22% 61.18% 30.30% 7.10% 25.19% 54.05%
## 933: 19.22% 61.18% 30.30% 7.10% 25.19% 54.05%
## 934: 19.26% 61.18% 30.30% 7.10% 25.34% 54.05%
## 935: 19.34% 62.35% 30.72% 7.02% 25.34% 54.05%
## 936: 19.34% 62.35% 30.72% 7.02% 25.34% 54.05%
## 937: 19.30% 62.35% 30.72% 6.94% 25.34% 54.05%
## 938: 19.30% 62.35% 30.30% 7.10% 25.34% 54.05%
## 939: 19.30% 62.35% 30.30% 7.10% 25.34% 54.05%
## 940: 19.26% 62.35% 30.30% 7.02% 25.34% 54.05%
## 941: 19.26% 62.35% 30.30% 7.02% 25.34% 54.05%
## 942: 19.26% 62.35% 30.30% 7.02% 25.34% 54.05%
## 943: 19.34% 62.35% 30.51% 7.02% 25.48% 54.05%
## 944: 19.34% 62.35% 30.51% 6.94% 25.63% 54.05%
## 945: 19.30% 62.35% 30.30% 7.02% 25.48% 54.05%
## 946: 19.30% 62.35% 30.51% 6.94% 25.48% 54.05%
## 947: 19.30% 62.35% 30.30% 7.02% 25.48% 54.05%
## 948: 19.26% 62.35% 30.08% 7.02% 25.48% 54.05%
## 949: 19.30% 62.35% 30.30% 7.02% 25.48% 54.05%
## 950: 19.26% 62.35% 30.30% 6.94% 25.63% 52.70%
## 951: 19.22% 62.35% 30.08% 7.02% 25.34% 54.05%
## 952: 19.22% 62.35% 30.08% 7.02% 25.34% 54.05%
## 953: 19.22% 62.35% 30.08% 7.02% 25.34% 54.05%
## 954: 19.30% 62.35% 30.08% 7.02% 25.48% 55.41%
## 955: 19.26% 62.35% 30.30% 7.02% 25.34% 54.05%
## 956: 19.38% 62.35% 30.30% 7.10% 25.48% 55.41%
## 957: 19.38% 62.35% 30.08% 7.18% 25.48% 55.41%
## 958: 19.30% 61.18% 30.08% 7.10% 25.48% 55.41%
## 959: 19.34% 62.35% 30.08% 7.10% 25.48% 55.41%
## 960: 19.38% 62.35% 30.08% 7.18% 25.48% 55.41%
## 961: 19.34% 61.18% 30.08% 7.18% 25.48% 55.41%
## 962: 19.42% 62.35% 30.30% 7.10% 25.63% 55.41%
## 963: 19.38% 61.18% 30.51% 7.10% 25.63% 54.05%
## 964: 19.46% 62.35% 30.51% 7.10% 25.63% 55.41%
## 965: 19.26% 61.18% 30.51% 6.94% 25.48% 54.05%
## 966: 19.30% 62.35% 30.51% 6.94% 25.48% 54.05%
## 967: 19.34% 62.35% 30.51% 6.94% 25.48% 55.41%
## 968: 19.26% 61.18% 30.51% 6.94% 25.34% 55.41%
## 969: 19.26% 60.00% 30.51% 6.94% 25.48% 55.41%
## 970: 19.30% 61.18% 30.51% 6.94% 25.48% 55.41%
## 971: 19.22% 61.18% 30.30% 6.94% 25.34% 55.41%
## 972: 19.26% 61.18% 30.51% 6.94% 25.34% 55.41%
## 973: 19.26% 62.35% 30.30% 6.94% 25.34% 55.41%
## 974: 19.22% 61.18% 30.30% 6.94% 25.34% 55.41%
## 975: 19.26% 61.18% 30.30% 6.94% 25.48% 55.41%
## 976: 19.30% 61.18% 30.30% 7.02% 25.48% 55.41%
## 977: 19.26% 61.18% 30.30% 7.02% 25.34% 55.41%
## 978: 19.22% 61.18% 30.30% 6.94% 25.34% 55.41%
## 979: 19.22% 61.18% 30.30% 6.94% 25.34% 55.41%
## 980: 19.22% 61.18% 30.30% 6.94% 25.34% 55.41%
## 981: 19.30% 61.18% 30.30% 7.02% 25.48% 55.41%
## 982: 19.26% 61.18% 30.30% 6.94% 25.48% 55.41%
## 983: 19.26% 61.18% 30.30% 6.94% 25.48% 55.41%
## 984: 19.26% 61.18% 30.30% 6.94% 25.48% 55.41%
## 985: 19.26% 61.18% 30.30% 6.94% 25.48% 55.41%
## 986: 19.26% 61.18% 30.51% 6.94% 25.34% 55.41%
## 987: 19.18% 61.18% 30.30% 6.86% 25.34% 55.41%
## 988: 19.18% 61.18% 30.30% 6.94% 25.19% 55.41%
## 989: 19.26% 61.18% 30.30% 6.94% 25.48% 55.41%
## 990: 19.30% 61.18% 30.30% 7.02% 25.48% 55.41%
## 991: 19.18% 61.18% 30.30% 6.94% 25.19% 55.41%
## 992: 19.30% 61.18% 30.51% 6.94% 25.48% 55.41%
## 993: 19.26% 61.18% 30.51% 6.94% 25.34% 55.41%
## 994: 19.18% 61.18% 30.30% 6.78% 25.48% 55.41%
## 995: 19.26% 61.18% 30.51% 6.86% 25.48% 55.41%
## 996: 19.22% 61.18% 30.30% 6.86% 25.48% 55.41%
## 997: 19.18% 61.18% 30.30% 6.78% 25.48% 55.41%
## 998: 19.14% 61.18% 30.30% 6.78% 25.34% 55.41%
## 999: 19.14% 61.18% 30.30% 6.78% 25.34% 55.41%
## 1000: 19.14% 61.18% 30.30% 6.78% 25.34% 55.41%test2 <- predict(model_rf2, newdata = test)
table(test2, test$Hospital.overall.rating)##
## test2 1 2 3 4 5
## 1 13 6 0 0 0
## 2 19 136 16 0 0
## 3 0 70 464 61 0
## 4 0 0 24 232 23
## 5 0 0 0 0 14summary(model_rf2)## Length Class Mode
## call 8 -none- call
## type 1 -none- character
## predicted 2570 factor numeric
## err.rate 6000 -none- numeric
## confusion 30 -none- numeric
## votes 12850 matrix numeric
## oob.times 2570 -none- numeric
## classes 5 -none- character
## importance 53 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 2570 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## terms 3 terms callconf_matrix2 <- confusionMatrix(test2, test$Hospital.overall.rating, positive = "Yes")
conf_matrix2## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5
## 1 13 6 0 0 0
## 2 19 136 16 0 0
## 3 0 70 464 61 0
## 4 0 0 24 232 23
## 5 0 0 0 0 14
##
## Overall Statistics
##
## Accuracy : 0.7968
## 95% CI : (0.7716, 0.8205)
## No Information Rate : 0.4675
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6823
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
## Sensitivity 0.40625 0.6415 0.9206 0.7918 0.37838
## Specificity 0.99426 0.9596 0.7718 0.9401 1.00000
## Pos Pred Value 0.68421 0.7953 0.7798 0.8315 1.00000
## Neg Pred Value 0.98206 0.9162 0.9172 0.9237 0.97838
## Prevalence 0.02968 0.1967 0.4675 0.2718 0.03432
## Detection Rate 0.01206 0.1262 0.4304 0.2152 0.01299
## Detection Prevalence 0.01763 0.1586 0.5519 0.2588 0.01299
## Balanced Accuracy 0.70026 0.8005 0.8462 0.8660 0.68919# Confusion Matrix and Statistics
#
# Reference
# Prediction 1 2 3 4 5
# 1 13 6 0 0 0
# 2 19 136 16 0 0
# 3 0 70 464 61 0
# 4 0 0 24 232 23
# 5 0 0 0 0 14
#
# Overall Statistics
#
# Accuracy : 0.7968
# 95% CI : (0.7716, 0.8205)
# No Information Rate : 0.4675
# P-Value [Acc > NIR] : < 2.2e-16
#
# Kappa : 0.6823
# Mcnemar's Test P-Value : NA
#
# Statistics by Class:
#
# Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
# Sensitivity 0.40625 0.6415 0.9206 0.7918 0.37838
# Specificity 0.99426 0.9596 0.7718 0.9401 1.00000
# Pos Pred Value 0.68421 0.7953 0.7798 0.8315 1.00000
# Neg Pred Value 0.98206 0.9162 0.9172 0.9237 0.97838
# Prevalence 0.02968 0.1967 0.4675 0.2718 0.03432
# Detection Rate 0.01206 0.1262 0.4304 0.2152 0.01299
# Detection Prevalence 0.01763 0.1586 0.5519 0.2588 0.01299
# Balanced Accuracy 0.70026 0.8005 0.8462 0.8660 0.68919
# Accuracy is 79.7%
model_rf3 <- randomForest(Hospital.overall.rating ~ ., data = train, promiximity = FALSE, ntree = 1500, mtry = 20, do.trace = TRUE, na.action = na.omit)## ntree OOB 1 2 3 4 5
## 1: 40.35% 62.07% 50.00% 34.98% 38.36% 68.97%
## 2: 41.89% 54.90% 51.03% 37.05% 42.02% 52.63%
## 3: 39.64% 45.59% 49.06% 35.18% 38.74% 54.72%
## 4: 37.63% 41.33% 45.54% 33.21% 37.41% 59.32%
## 5: 36.82% 46.84% 45.73% 31.62% 36.91% 56.06%
## 6: 36.08% 53.57% 43.95% 29.77% 37.46% 58.33%
## 7: 35.02% 51.19% 45.18% 28.17% 36.26% 55.56%
## 8: 35.05% 42.86% 46.44% 27.28% 37.67% 60.81%
## 9: 33.84% 47.62% 44.54% 25.67% 37.27% 56.76%
## 10: 33.95% 48.24% 45.53% 25.42% 37.25% 58.11%
## 11: 32.73% 49.41% 42.89% 23.87% 37.65% 55.41%
## 12: 31.55% 44.71% 43.10% 22.19% 36.94% 54.05%
## 13: 30.87% 45.88% 42.04% 21.33% 36.42% 55.41%
## 14: 30.70% 43.53% 42.80% 21.72% 35.07% 52.70%
## 15: 29.79% 47.06% 40.47% 20.99% 34.18% 52.70%
## 16: 29.77% 47.06% 39.62% 21.06% 34.58% 52.70%
## 17: 28.05% 45.88% 36.86% 19.79% 32.49% 52.70%
## 18: 27.94% 45.88% 37.92% 19.01% 32.49% 55.41%
## 19: 28.13% 48.24% 37.92% 18.93% 32.94% 56.76%
## 20: 27.74% 49.41% 38.35% 17.90% 32.79% 58.11%
## 21: 27.16% 47.06% 37.71% 18.22% 31.00% 55.41%
## 22: 26.81% 47.06% 37.92% 17.19% 31.45% 55.41%
## 23: 26.34% 49.41% 37.08% 16.48% 31.59% 52.70%
## 24: 26.50% 51.76% 37.29% 17.03% 30.85% 51.35%
## 25: 26.46% 55.29% 37.08% 16.09% 31.45% 58.11%
## 26: 25.60% 51.76% 34.96% 16.48% 29.66% 55.41%
## 27: 25.72% 49.41% 36.65% 15.69% 30.85% 54.05%
## 28: 24.63% 48.24% 35.59% 14.67% 29.81% 51.35%
## 29: 24.47% 49.41% 32.63% 14.91% 30.25% 55.41%
## 30: 24.12% 50.59% 34.11% 13.64% 30.10% 55.41%
## 31: 24.16% 49.41% 35.59% 13.88% 29.21% 52.70%
## 32: 23.85% 51.76% 32.20% 13.88% 29.81% 55.41%
## 33: 24.09% 52.94% 32.84% 13.96% 29.51% 59.46%
## 34: 23.85% 49.41% 33.05% 13.56% 29.81% 58.11%
## 35: 23.70% 51.76% 33.05% 13.64% 28.91% 56.76%
## 36: 23.07% 52.94% 32.20% 12.07% 29.96% 56.76%
## 37: 23.07% 52.94% 33.05% 12.07% 29.36% 56.76%
## 38: 23.04% 54.12% 31.78% 12.07% 29.66% 59.46%
## 39: 22.72% 51.76% 31.99% 12.07% 28.91% 56.76%
## 40: 22.96% 51.76% 33.26% 12.30% 28.46% 56.76%
## 41: 22.45% 51.76% 32.20% 11.75% 28.46% 55.41%
## 42: 22.80% 54.12% 32.20% 12.30% 28.32% 56.76%
## 43: 22.65% 51.76% 33.05% 11.99% 28.02% 56.76%
## 44: 22.65% 50.59% 32.20% 12.70% 27.57% 55.41%
## 45: 22.76% 51.76% 31.99% 12.85% 27.72% 55.41%
## 46: 22.45% 52.94% 31.57% 12.07% 28.17% 55.41%
## 47: 22.30% 52.94% 31.78% 11.99% 27.42% 56.76%
## 48: 21.95% 52.94% 30.08% 11.67% 28.02% 55.41%
## 49: 22.06% 54.12% 30.93% 11.44% 27.87% 58.11%
## 50: 22.26% 54.12% 32.42% 11.28% 27.87% 58.11%
## 51: 22.18% 54.12% 32.63% 11.28% 27.87% 54.05%
## 52: 22.10% 54.12% 33.47% 11.12% 27.27% 54.05%
## 53: 22.49% 55.29% 33.26% 11.51% 27.87% 55.41%
## 54: 22.30% 54.12% 32.63% 11.83% 27.12% 55.41%
## 55: 21.87% 55.29% 32.20% 11.20% 26.97% 54.05%
## 56: 22.06% 57.65% 33.05% 11.04% 26.97% 55.41%
## 57: 22.02% 57.65% 31.57% 11.12% 27.72% 55.41%
## 58: 21.83% 55.29% 31.78% 11.04% 27.27% 55.41%
## 59: 21.63% 51.76% 32.20% 10.96% 26.83% 55.41%
## 60: 21.75% 56.47% 31.99% 10.73% 27.27% 55.41%
## 61: 21.83% 52.94% 32.63% 10.96% 27.12% 55.41%
## 62: 21.56% 54.12% 32.63% 10.65% 26.53% 55.41%
## 63: 21.83% 55.29% 32.84% 11.04% 26.53% 55.41%
## 64: 21.83% 52.94% 33.05% 10.73% 27.27% 55.41%
## 65: 21.91% 52.94% 34.53% 10.65% 26.83% 54.05%
## 66: 21.71% 52.94% 33.90% 10.41% 26.83% 55.41%
## 67: 21.91% 55.29% 34.75% 10.49% 26.68% 54.05%
## 68: 21.79% 52.94% 34.11% 10.73% 26.53% 54.05%
## 69: 21.67% 52.94% 33.47% 10.65% 26.53% 55.41%
## 70: 21.60% 48.24% 33.47% 10.41% 27.27% 55.41%
## 71: 21.67% 50.59% 33.26% 10.96% 26.38% 55.41%
## 72: 21.98% 54.12% 33.26% 10.88% 27.27% 55.41%
## 73: 21.87% 52.94% 33.05% 10.73% 27.42% 55.41%
## 74: 21.91% 51.76% 33.26% 10.96% 27.27% 54.05%
## 75: 22.10% 52.94% 34.32% 10.65% 27.57% 55.41%
## 76: 21.91% 54.12% 33.26% 10.73% 27.27% 55.41%
## 77: 21.32% 52.94% 33.26% 10.09% 26.53% 54.05%
## 78: 21.48% 55.29% 33.47% 10.17% 26.38% 55.41%
## 79: 21.40% 56.47% 32.42% 10.17% 26.83% 54.05%
## 80: 21.36% 55.29% 32.42% 9.94% 27.27% 54.05%
## 81: 21.44% 60.00% 32.63% 9.94% 26.97% 52.70%
## 82: 21.56% 58.82% 33.05% 9.94% 27.12% 54.05%
## 83: 21.28% 56.47% 32.84% 9.54% 27.27% 54.05%
## 84: 21.48% 56.47% 33.26% 9.86% 27.12% 54.05%
## 85: 21.13% 57.65% 32.20% 9.54% 26.97% 54.05%
## 86: 21.05% 56.47% 31.78% 9.38% 27.42% 54.05%
## 87: 21.09% 58.82% 31.99% 9.31% 27.57% 51.35%
## 88: 21.17% 56.47% 32.63% 9.38% 27.42% 52.70%
## 89: 21.05% 54.12% 31.57% 9.54% 27.87% 51.35%
## 90: 21.09% 54.12% 32.63% 9.23% 28.02% 50.00%
## 91: 20.93% 56.47% 31.78% 9.23% 27.72% 50.00%
## 92: 21.32% 56.47% 32.42% 9.62% 28.02% 50.00%
## 93: 21.05% 57.65% 31.99% 9.46% 27.42% 50.00%
## 94: 20.89% 57.65% 31.78% 9.31% 27.27% 50.00%
## 95: 21.09% 57.65% 31.78% 9.62% 27.27% 51.35%
## 96: 20.86% 57.65% 32.42% 9.31% 26.68% 50.00%
## 97: 20.86% 55.29% 33.05% 9.15% 26.68% 51.35%
## 98: 21.09% 57.65% 32.42% 9.62% 26.83% 51.35%
## 99: 20.74% 56.47% 32.20% 8.99% 26.97% 51.35%
## 100: 20.89% 58.82% 31.99% 9.23% 26.97% 51.35%
## 101: 20.82% 58.82% 32.42% 9.15% 26.53% 51.35%
## 102: 20.74% 56.47% 31.57% 9.46% 26.53% 51.35%
## 103: 20.51% 56.47% 31.57% 8.75% 26.97% 51.35%
## 104: 20.58% 57.65% 31.36% 9.07% 26.53% 52.70%
## 105: 20.78% 57.65% 31.57% 9.15% 26.97% 52.70%
## 106: 20.43% 56.47% 31.36% 8.68% 26.83% 52.70%
## 107: 20.78% 57.65% 31.57% 9.07% 27.12% 52.70%
## 108: 20.74% 57.65% 31.36% 8.83% 27.57% 52.70%
## 109: 20.97% 60.00% 31.78% 9.23% 27.12% 52.70%
## 110: 20.62% 57.65% 30.93% 9.23% 26.68% 52.70%
## 111: 20.82% 56.47% 31.78% 9.38% 26.83% 51.35%
## 112: 20.74% 58.82% 31.78% 8.99% 26.97% 51.35%
## 113: 20.97% 57.65% 31.36% 9.54% 27.27% 51.35%
## 114: 20.89% 57.65% 31.36% 9.31% 27.42% 51.35%
## 115: 20.66% 57.65% 30.72% 9.31% 26.97% 51.35%
## 116: 20.86% 57.65% 31.14% 9.15% 27.72% 51.35%
## 117: 20.89% 58.82% 31.14% 9.38% 27.27% 51.35%
## 118: 20.54% 58.82% 31.14% 8.91% 26.68% 52.70%
## 119: 20.66% 57.65% 30.72% 9.23% 26.97% 52.70%
## 120: 20.19% 58.82% 29.87% 8.75% 26.53% 52.70%
## 121: 20.31% 57.65% 30.30% 8.75% 26.83% 52.70%
## 122: 20.31% 57.65% 30.72% 8.75% 26.38% 54.05%
## 123: 20.31% 57.65% 29.87% 8.91% 26.68% 54.05%
## 124: 20.31% 55.29% 30.93% 8.99% 26.08% 54.05%
## 125: 20.23% 55.29% 30.30% 8.91% 26.53% 52.70%
## 126: 20.04% 55.29% 30.51% 8.60% 26.23% 52.70%
## 127: 20.23% 56.47% 30.30% 8.83% 26.53% 52.70%
## 128: 20.58% 56.47% 31.36% 8.68% 27.27% 54.05%
## 129: 20.27% 56.47% 31.14% 8.60% 26.53% 52.70%
## 130: 20.27% 55.29% 30.51% 8.68% 26.83% 54.05%
## 131: 20.16% 55.29% 31.14% 8.60% 26.23% 52.70%
## 132: 20.16% 56.47% 31.36% 8.52% 25.93% 54.05%
## 133: 20.23% 58.82% 30.93% 8.52% 26.23% 54.05%
## 134: 20.27% 57.65% 30.72% 8.68% 26.68% 51.35%
## 135: 20.47% 56.47% 31.57% 8.83% 26.38% 54.05%
## 136: 20.39% 57.65% 31.57% 8.52% 26.68% 52.70%
## 137: 20.39% 56.47% 31.36% 8.91% 26.38% 51.35%
## 138: 20.08% 57.65% 30.51% 8.52% 26.08% 54.05%
## 139: 20.27% 60.00% 31.14% 8.44% 26.23% 54.05%
## 140: 20.39% 58.82% 31.36% 8.28% 26.97% 54.05%
## 141: 20.47% 60.00% 31.14% 8.60% 26.83% 52.70%
## 142: 20.23% 57.65% 31.36% 8.36% 26.38% 54.05%
## 143: 20.43% 57.65% 31.14% 8.75% 26.53% 54.05%
## 144: 20.23% 57.65% 31.14% 8.44% 26.53% 52.70%
## 145: 20.43% 57.65% 31.57% 8.52% 26.68% 54.05%
## 146: 20.58% 57.65% 31.36% 8.60% 27.27% 54.05%
## 147: 20.58% 57.65% 31.36% 8.52% 27.57% 52.70%
## 148: 20.16% 57.65% 31.78% 8.12% 26.38% 52.70%
## 149: 20.19% 57.65% 31.14% 8.12% 26.83% 54.05%
## 150: 20.31% 57.65% 30.72% 8.20% 27.42% 54.05%
## 151: 20.51% 57.65% 31.57% 8.20% 27.57% 54.05%
## 152: 20.39% 57.65% 31.78% 8.04% 27.42% 52.70%
## 153: 20.23% 57.65% 30.72% 8.36% 26.97% 52.70%
## 154: 20.27% 57.65% 31.14% 8.12% 27.27% 52.70%
## 155: 20.19% 57.65% 31.14% 8.36% 26.53% 52.70%
## 156: 20.39% 57.65% 31.57% 8.68% 26.38% 52.70%
## 157: 20.19% 57.65% 30.93% 8.60% 26.23% 52.70%
## 158: 20.31% 57.65% 30.93% 8.52% 26.83% 52.70%
## 159: 20.27% 57.65% 31.14% 8.44% 26.53% 54.05%
## 160: 20.27% 57.65% 31.14% 8.36% 26.83% 52.70%
## 161: 20.35% 57.65% 31.14% 8.52% 26.83% 52.70%
## 162: 20.58% 58.82% 31.57% 8.44% 27.27% 54.05%
## 163: 20.54% 56.47% 31.99% 8.44% 27.12% 54.05%
## 164: 20.43% 57.65% 32.20% 8.12% 26.97% 54.05%
## 165: 20.31% 57.65% 31.78% 8.28% 26.53% 54.05%
## 166: 20.47% 57.65% 32.20% 8.44% 26.53% 54.05%
## 167: 20.47% 57.65% 31.99% 8.52% 26.53% 54.05%
## 168: 20.66% 57.65% 31.78% 8.68% 27.12% 54.05%
## 169: 20.54% 57.65% 31.57% 8.68% 26.83% 54.05%
## 170: 20.35% 58.82% 31.14% 8.44% 26.68% 54.05%
## 171: 20.51% 57.65% 30.72% 8.60% 27.57% 52.70%
## 172: 20.27% 57.65% 31.36% 8.28% 26.83% 52.70%
## 173: 20.51% 57.65% 30.93% 8.68% 27.27% 52.70%
## 174: 20.43% 57.65% 30.72% 8.75% 26.97% 52.70%
## 175: 20.62% 57.65% 31.36% 8.44% 27.87% 52.70%
## 176: 20.39% 57.65% 31.14% 8.36% 27.27% 52.70%
## 177: 20.47% 57.65% 31.36% 8.60% 26.97% 52.70%
## 178: 20.31% 57.65% 31.36% 8.44% 26.68% 52.70%
## 179: 20.16% 57.65% 31.36% 8.20% 26.53% 52.70%
## 180: 20.16% 57.65% 30.72% 8.36% 26.68% 52.70%
## 181: 19.96% 57.65% 29.87% 8.44% 26.38% 52.70%
## 182: 20.23% 58.82% 30.93% 8.52% 26.38% 52.70%
## 183: 20.27% 57.65% 30.51% 8.75% 26.38% 54.05%
## 184: 20.51% 58.82% 30.72% 8.68% 27.12% 54.05%
## 185: 20.08% 58.82% 29.66% 8.52% 26.68% 52.70%
## 186: 20.16% 58.82% 30.30% 8.60% 26.38% 52.70%
## 187: 20.12% 58.82% 30.72% 8.36% 26.38% 52.70%
## 188: 20.16% 58.82% 30.72% 8.36% 26.53% 52.70%
## 189: 20.31% 58.82% 30.51% 8.52% 26.83% 54.05%
## 190: 20.04% 58.82% 29.66% 8.44% 26.68% 52.70%
## 191: 20.16% 58.82% 31.14% 8.28% 26.53% 51.35%
## 192: 19.96% 58.82% 30.30% 8.12% 26.53% 52.70%
## 193: 20.12% 58.82% 30.72% 8.36% 26.38% 52.70%
## 194: 20.00% 58.82% 30.30% 8.12% 26.53% 54.05%
## 195: 19.96% 58.82% 30.08% 8.44% 26.08% 52.70%
## 196: 20.00% 58.82% 30.51% 8.36% 26.08% 52.70%
## 197: 20.16% 58.82% 30.72% 8.36% 26.53% 52.70%
## 198: 20.12% 58.82% 30.30% 8.36% 26.68% 52.70%
## 199: 19.96% 58.82% 29.87% 8.44% 26.23% 52.70%
## 200: 19.92% 58.82% 30.93% 7.97% 26.23% 52.70%
## 201: 19.88% 58.82% 30.30% 8.12% 26.23% 52.70%
## 202: 19.92% 58.82% 30.08% 8.04% 26.68% 52.70%
## 203: 20.08% 58.82% 30.30% 8.36% 26.53% 52.70%
## 204: 20.00% 58.82% 29.87% 8.28% 26.68% 52.70%
## 205: 20.00% 58.82% 29.87% 8.20% 26.97% 51.35%
## 206: 19.84% 58.82% 29.66% 8.04% 26.53% 54.05%
## 207: 19.92% 58.82% 29.66% 8.28% 26.38% 54.05%
## 208: 20.04% 58.82% 30.08% 8.28% 26.53% 54.05%
## 209: 20.12% 58.82% 29.87% 8.52% 26.53% 54.05%
## 210: 19.88% 58.82% 29.24% 8.52% 26.23% 52.70%
## 211: 20.00% 58.82% 29.66% 8.44% 26.38% 54.05%
## 212: 19.96% 58.82% 29.87% 8.28% 26.53% 52.70%
## 213: 20.00% 58.82% 30.08% 8.12% 26.83% 52.70%
## 214: 20.00% 58.82% 29.87% 8.28% 26.68% 52.70%
## 215: 19.77% 58.82% 28.81% 8.12% 26.83% 52.70%
## 216: 19.96% 58.82% 29.03% 8.44% 26.83% 52.70%
## 217: 19.92% 58.82% 29.45% 8.04% 27.12% 52.70%
## 218: 20.04% 58.82% 29.87% 8.36% 26.53% 54.05%
## 219: 19.88% 58.82% 29.87% 8.20% 26.38% 52.70%
## 220: 19.96% 58.82% 29.66% 8.36% 26.53% 52.70%
## 221: 20.12% 58.82% 30.30% 8.28% 26.68% 54.05%
## 222: 19.88% 58.82% 29.66% 8.12% 26.53% 54.05%
## 223: 20.16% 58.82% 30.08% 8.44% 26.68% 54.05%
## 224: 19.96% 58.82% 30.51% 8.04% 26.53% 52.70%
## 225: 20.08% 58.82% 30.51% 8.12% 26.97% 51.35%
## 226: 19.92% 58.82% 30.30% 8.04% 26.68% 51.35%
## 227: 19.88% 58.82% 30.30% 7.97% 26.68% 51.35%
## 228: 20.08% 58.82% 30.30% 8.20% 26.97% 51.35%
## 229: 19.96% 58.82% 29.66% 8.12% 27.12% 51.35%
## 230: 19.92% 58.82% 30.08% 8.20% 26.53% 51.35%
## 231: 20.00% 58.82% 30.08% 8.12% 26.97% 51.35%
## 232: 19.73% 58.82% 29.87% 7.73% 26.83% 51.35%
## 233: 19.84% 57.65% 29.66% 8.12% 26.83% 51.35%
## 234: 19.84% 58.82% 29.66% 7.97% 26.97% 51.35%
## 235: 19.84% 57.65% 29.87% 8.04% 26.83% 51.35%
## 236: 19.81% 58.82% 29.87% 7.89% 26.83% 51.35%
## 237: 19.77% 58.82% 29.45% 7.81% 26.97% 52.70%
## 238: 19.77% 58.82% 29.87% 7.81% 26.83% 51.35%
## 239: 19.69% 58.82% 29.45% 7.81% 26.83% 51.35%
## 240: 19.88% 57.65% 29.66% 8.20% 26.83% 51.35%
## 241: 19.81% 58.82% 29.45% 8.04% 26.83% 51.35%
## 242: 19.81% 58.82% 29.24% 8.04% 26.83% 52.70%
## 243: 19.81% 58.82% 29.45% 8.04% 26.68% 52.70%
## 244: 20.12% 58.82% 30.08% 8.44% 26.83% 51.35%
## 245: 20.08% 57.65% 30.30% 8.20% 26.97% 52.70%
## 246: 19.73% 57.65% 29.87% 7.89% 26.53% 52.70%
## 247: 19.81% 58.82% 29.87% 7.89% 26.68% 52.70%
## 248: 19.88% 58.82% 30.08% 7.57% 27.42% 52.70%
## 249: 19.81% 57.65% 29.45% 7.81% 27.27% 52.70%
## 250: 20.08% 57.65% 29.87% 8.28% 27.12% 52.70%
## 251: 19.88% 57.65% 30.30% 7.89% 26.83% 52.70%
## 252: 20.19% 57.65% 30.72% 8.12% 27.27% 52.70%
## 253: 20.08% 57.65% 30.30% 8.04% 27.27% 52.70%
## 254: 19.92% 57.65% 30.51% 8.04% 26.53% 52.70%
## 255: 20.16% 57.65% 31.14% 8.12% 26.83% 52.70%
## 256: 20.12% 57.65% 30.93% 8.20% 26.68% 52.70%
## 257: 20.04% 57.65% 30.93% 8.04% 26.68% 52.70%
## 258: 20.08% 57.65% 30.93% 8.12% 26.68% 52.70%
## 259: 20.00% 58.82% 30.93% 8.04% 26.38% 52.70%
## 260: 20.12% 58.82% 31.14% 8.04% 26.68% 52.70%
## 261: 20.12% 57.65% 31.14% 8.04% 26.83% 52.70%
## 262: 20.00% 57.65% 31.14% 7.89% 26.68% 52.70%
## 263: 20.00% 58.82% 31.36% 7.97% 26.23% 52.70%
## 264: 20.00% 58.82% 31.36% 7.89% 26.38% 52.70%
## 265: 20.16% 58.82% 31.78% 8.12% 26.23% 52.70%
## 266: 19.96% 60.00% 30.72% 8.12% 26.08% 52.70%
## 267: 20.04% 60.00% 31.57% 7.97% 26.08% 52.70%
## 268: 19.96% 60.00% 31.57% 7.81% 26.08% 52.70%
## 269: 19.92% 60.00% 30.93% 7.81% 26.38% 52.70%
## 270: 19.92% 60.00% 30.93% 7.89% 26.23% 52.70%
## 271: 19.65% 58.82% 30.30% 7.73% 26.08% 52.70%
## 272: 19.81% 58.82% 30.72% 7.89% 26.08% 52.70%
## 273: 19.69% 58.82% 30.51% 7.81% 25.93% 52.70%
## 274: 19.61% 58.82% 30.30% 7.73% 25.93% 52.70%
## 275: 19.77% 57.65% 30.51% 7.97% 26.08% 52.70%
## 276: 19.88% 58.82% 31.36% 7.65% 26.38% 52.70%
## 277: 20.08% 60.00% 32.20% 7.73% 26.23% 52.70%
## 278: 20.04% 60.00% 31.99% 7.73% 26.23% 52.70%
## 279: 20.27% 60.00% 32.20% 7.97% 26.53% 52.70%
## 280: 20.31% 60.00% 31.78% 8.12% 26.68% 52.70%
## 281: 20.16% 60.00% 31.57% 8.04% 26.38% 52.70%
## 282: 19.88% 60.00% 31.36% 7.65% 26.23% 52.70%
## 283: 19.96% 60.00% 31.36% 7.81% 26.38% 51.35%
## 284: 19.84% 60.00% 30.93% 7.89% 25.93% 52.70%
## 285: 19.96% 60.00% 31.36% 7.81% 26.23% 52.70%
## 286: 20.00% 60.00% 31.57% 7.73% 26.38% 52.70%
## 287: 19.92% 60.00% 31.36% 7.73% 26.23% 52.70%
## 288: 20.12% 61.18% 31.57% 7.97% 26.23% 52.70%
## 289: 20.16% 60.00% 31.57% 8.12% 26.23% 52.70%
## 290: 20.04% 60.00% 31.36% 7.89% 26.38% 52.70%
## 291: 20.00% 61.18% 31.14% 7.81% 26.38% 52.70%
## 292: 19.88% 60.00% 31.36% 7.81% 25.93% 52.70%
## 293: 19.88% 61.18% 31.14% 7.97% 25.63% 52.70%
## 294: 19.92% 61.18% 31.36% 7.81% 25.93% 52.70%
## 295: 20.08% 61.18% 31.36% 7.97% 26.23% 52.70%
## 296: 19.96% 61.18% 30.93% 8.04% 25.93% 52.70%
## 297: 19.96% 61.18% 31.14% 8.04% 25.78% 52.70%
## 298: 19.92% 61.18% 30.93% 8.04% 25.78% 52.70%
## 299: 20.00% 61.18% 31.14% 8.12% 25.78% 52.70%
## 300: 19.92% 61.18% 30.93% 8.12% 25.78% 51.35%
## 301: 19.84% 61.18% 31.36% 7.81% 25.78% 51.35%
## 302: 19.73% 61.18% 30.93% 7.89% 25.63% 50.00%
## 303: 20.00% 61.18% 30.93% 7.97% 26.38% 51.35%
## 304: 19.84% 61.18% 30.93% 7.89% 25.93% 51.35%
## 305: 19.88% 61.18% 30.72% 7.89% 26.23% 51.35%
## 306: 19.92% 61.18% 30.93% 7.97% 26.08% 51.35%
## 307: 19.96% 61.18% 31.14% 7.89% 26.23% 51.35%
## 308: 19.84% 61.18% 30.93% 7.81% 26.08% 51.35%
## 309: 20.00% 61.18% 30.93% 8.04% 26.23% 51.35%
## 310: 20.12% 61.18% 31.14% 8.12% 26.38% 51.35%
## 311: 20.04% 61.18% 31.36% 7.97% 26.23% 51.35%
## 312: 20.12% 61.18% 31.78% 7.89% 26.38% 51.35%
## 313: 19.84% 61.18% 31.36% 7.81% 25.78% 51.35%
## 314: 19.84% 61.18% 31.14% 7.81% 25.93% 51.35%
## 315: 19.84% 61.18% 31.57% 7.81% 25.78% 50.00%
## 316: 19.92% 61.18% 31.57% 7.81% 26.08% 50.00%
## 317: 19.92% 61.18% 31.57% 7.73% 26.23% 50.00%
## 318: 19.92% 61.18% 31.78% 7.89% 25.78% 50.00%
## 319: 19.84% 61.18% 31.78% 7.65% 25.93% 50.00%
## 320: 19.84% 61.18% 31.57% 7.81% 25.78% 50.00%
## 321: 19.73% 61.18% 31.57% 7.73% 25.48% 50.00%
## 322: 19.65% 61.18% 31.36% 7.65% 25.48% 50.00%
## 323: 19.69% 61.18% 31.57% 7.65% 25.48% 50.00%
## 324: 19.88% 61.18% 31.99% 7.65% 25.93% 50.00%
## 325: 19.81% 61.18% 31.78% 7.65% 25.78% 50.00%
## 326: 19.84% 61.18% 31.78% 7.65% 25.93% 50.00%
## 327: 19.77% 61.18% 31.99% 7.65% 25.48% 50.00%
## 328: 19.84% 61.18% 31.78% 7.73% 25.78% 50.00%
## 329: 19.73% 61.18% 31.36% 7.65% 25.78% 50.00%
## 330: 19.84% 61.18% 31.57% 7.73% 25.93% 50.00%
## 331: 19.69% 61.18% 31.57% 7.65% 25.48% 50.00%
## 332: 19.61% 61.18% 31.36% 7.65% 25.34% 50.00%
## 333: 19.53% 61.18% 31.36% 7.65% 25.04% 50.00%
## 334: 19.69% 61.18% 31.36% 7.65% 25.63% 50.00%
## 335: 19.81% 61.18% 31.36% 7.73% 25.93% 50.00%
## 336: 19.84% 62.35% 31.14% 7.65% 26.23% 50.00%
## 337: 19.73% 61.18% 31.14% 7.65% 25.93% 50.00%
## 338: 19.81% 61.18% 31.14% 7.81% 25.93% 50.00%
## 339: 19.77% 61.18% 31.14% 7.73% 25.93% 50.00%
## 340: 19.69% 61.18% 31.36% 7.73% 25.48% 50.00%
## 341: 19.84% 61.18% 31.36% 7.73% 26.08% 50.00%
## 342: 19.92% 62.35% 31.78% 7.57% 26.23% 50.00%
## 343: 19.84% 61.18% 31.36% 7.73% 26.08% 50.00%
## 344: 19.88% 61.18% 31.57% 7.81% 25.93% 50.00%
## 345: 19.81% 62.35% 31.14% 7.73% 25.93% 50.00%
## 346: 19.84% 62.35% 31.14% 7.81% 25.93% 50.00%
## 347: 19.92% 62.35% 31.57% 7.73% 26.08% 50.00%
## 348: 19.92% 62.35% 31.57% 7.73% 26.08% 50.00%
## 349: 20.00% 62.35% 31.78% 7.81% 26.08% 50.00%
## 350: 19.96% 62.35% 31.78% 7.89% 25.78% 50.00%
## 351: 19.96% 62.35% 31.57% 7.89% 25.93% 50.00%
## 352: 19.88% 62.35% 31.78% 7.65% 25.93% 50.00%
## 353: 19.96% 62.35% 32.20% 7.73% 25.78% 50.00%
## 354: 19.96% 62.35% 31.99% 7.65% 26.08% 50.00%
## 355: 20.00% 62.35% 31.78% 7.81% 26.08% 50.00%
## 356: 19.84% 62.35% 31.78% 7.65% 25.78% 50.00%
## 357: 19.73% 62.35% 31.78% 7.57% 25.48% 50.00%
## 358: 19.84% 62.35% 31.99% 7.65% 25.48% 51.35%
## 359: 19.84% 62.35% 31.99% 7.49% 25.78% 51.35%
## 360: 20.04% 63.53% 32.20% 7.65% 25.93% 51.35%
## 361: 20.00% 63.53% 31.99% 7.57% 26.08% 51.35%
## 362: 20.00% 62.35% 32.20% 7.49% 26.23% 51.35%
## 363: 20.08% 63.53% 32.42% 7.49% 26.08% 52.70%
## 364: 20.04% 63.53% 32.42% 7.49% 25.93% 52.70%
## 365: 20.19% 62.35% 32.84% 7.73% 25.93% 52.70%
## 366: 20.12% 63.53% 32.84% 7.57% 25.78% 52.70%
## 367: 20.04% 63.53% 32.63% 7.49% 25.78% 52.70%
## 368: 20.12% 63.53% 32.84% 7.57% 25.78% 52.70%
## 369: 20.12% 63.53% 32.63% 7.57% 25.93% 52.70%
## 370: 20.04% 63.53% 32.42% 7.49% 25.93% 52.70%
## 371: 20.12% 63.53% 32.20% 7.65% 26.08% 52.70%
## 372: 20.12% 63.53% 32.20% 7.65% 26.08% 52.70%
## 373: 20.08% 63.53% 31.99% 7.73% 25.93% 52.70%
## 374: 20.27% 63.53% 32.63% 7.73% 26.23% 52.70%
## 375: 20.19% 63.53% 32.20% 7.73% 26.23% 52.70%
## 376: 20.27% 63.53% 32.63% 7.73% 26.23% 52.70%
## 377: 20.23% 63.53% 32.20% 7.89% 26.08% 52.70%
## 378: 20.08% 63.53% 32.20% 7.73% 25.78% 52.70%
## 379: 20.00% 63.53% 31.78% 7.73% 25.78% 52.70%
## 380: 19.84% 62.35% 31.57% 7.65% 25.63% 52.70%
## 381: 19.84% 62.35% 31.78% 7.65% 25.48% 52.70%
## 382: 20.08% 62.35% 32.42% 7.89% 25.48% 52.70%
## 383: 20.00% 63.53% 32.20% 7.57% 25.78% 52.70%
## 384: 20.00% 63.53% 31.99% 7.73% 25.63% 52.70%
## 385: 19.84% 63.53% 31.78% 7.41% 25.78% 52.70%
## 386: 19.81% 63.53% 31.57% 7.33% 25.93% 52.70%
## 387: 19.88% 63.53% 31.99% 7.41% 25.78% 52.70%
## 388: 19.96% 63.53% 31.78% 7.49% 26.08% 52.70%
## 389: 19.88% 63.53% 31.78% 7.41% 25.93% 52.70%
## 390: 19.96% 63.53% 31.99% 7.41% 26.08% 52.70%
## 391: 19.96% 63.53% 31.78% 7.41% 26.23% 52.70%
## 392: 20.00% 63.53% 31.99% 7.49% 26.08% 52.70%
## 393: 19.96% 63.53% 31.57% 7.49% 26.23% 52.70%
## 394: 19.69% 62.35% 31.57% 7.41% 25.48% 52.70%
## 395: 19.65% 62.35% 31.36% 7.41% 25.48% 52.70%
## 396: 19.77% 63.53% 31.78% 7.41% 25.48% 52.70%
## 397: 19.77% 62.35% 31.78% 7.41% 25.63% 52.70%
## 398: 19.77% 62.35% 31.57% 7.41% 25.78% 52.70%
## 399: 19.73% 62.35% 31.36% 7.49% 25.63% 52.70%
## 400: 19.73% 62.35% 31.57% 7.41% 25.63% 52.70%
## 401: 19.65% 62.35% 31.14% 7.41% 25.63% 52.70%
## 402: 19.69% 62.35% 31.36% 7.49% 25.48% 52.70%
## 403: 19.81% 62.35% 31.78% 7.57% 25.48% 52.70%
## 404: 19.84% 62.35% 31.57% 7.57% 25.78% 52.70%
## 405: 19.73% 62.35% 31.36% 7.41% 25.78% 52.70%
## 406: 19.81% 62.35% 31.57% 7.41% 25.93% 52.70%
## 407: 19.92% 62.35% 31.78% 7.57% 25.93% 52.70%
## 408: 19.96% 63.53% 31.99% 7.49% 25.93% 52.70%
## 409: 19.92% 62.35% 31.57% 7.65% 25.93% 52.70%
## 410: 19.77% 62.35% 31.36% 7.49% 25.78% 52.70%
## 411: 20.04% 63.53% 31.78% 7.73% 25.93% 52.70%
## 412: 19.73% 63.53% 30.72% 7.57% 25.78% 52.70%
## 413: 19.73% 62.35% 31.14% 7.41% 25.93% 52.70%
## 414: 19.61% 63.53% 30.72% 7.49% 25.48% 52.70%
## 415: 19.77% 62.35% 31.57% 7.41% 25.78% 52.70%
## 416: 19.81% 63.53% 31.14% 7.57% 25.78% 52.70%
## 417: 19.73% 62.35% 30.72% 7.57% 25.93% 52.70%
## 418: 19.69% 62.35% 31.36% 7.33% 25.78% 52.70%
## 419: 19.69% 63.53% 30.93% 7.41% 25.78% 52.70%
## 420: 19.65% 63.53% 31.14% 7.33% 25.63% 52.70%
## 421: 19.73% 63.53% 31.36% 7.41% 25.63% 52.70%
## 422: 19.77% 63.53% 31.57% 7.18% 26.08% 52.70%
## 423: 19.65% 63.53% 31.14% 7.26% 25.78% 52.70%
## 424: 19.73% 63.53% 30.51% 7.49% 26.08% 52.70%
## 425: 19.73% 63.53% 30.93% 7.26% 26.23% 52.70%
## 426: 19.81% 63.53% 30.72% 7.41% 26.38% 52.70%
## 427: 19.92% 63.53% 31.14% 7.41% 26.53% 52.70%
## 428: 19.81% 63.53% 31.14% 7.33% 26.23% 52.70%
## 429: 19.77% 63.53% 30.72% 7.49% 26.08% 52.70%
## 430: 19.81% 63.53% 30.93% 7.49% 26.08% 52.70%
## 431: 19.77% 63.53% 30.93% 7.41% 26.08% 52.70%
## 432: 19.69% 63.53% 30.51% 7.49% 25.93% 52.70%
## 433: 19.73% 63.53% 30.93% 7.33% 26.08% 52.70%
## 434: 19.61% 63.53% 30.72% 7.26% 26.08% 51.35%
## 435: 19.84% 63.53% 31.14% 7.49% 26.08% 52.70%
## 436: 19.81% 63.53% 30.72% 7.49% 26.23% 52.70%
## 437: 19.69% 63.53% 30.51% 7.41% 26.08% 52.70%
## 438: 19.65% 63.53% 30.30% 7.41% 26.08% 52.70%
## 439: 19.61% 63.53% 30.30% 7.49% 25.78% 52.70%
## 440: 19.73% 63.53% 30.93% 7.49% 25.78% 52.70%
## 441: 19.73% 63.53% 30.93% 7.49% 25.78% 52.70%
## 442: 19.57% 63.53% 30.30% 7.33% 26.08% 51.35%
## 443: 19.57% 63.53% 30.30% 7.41% 25.78% 52.70%
## 444: 19.65% 63.53% 30.51% 7.41% 25.93% 52.70%
## 445: 19.73% 63.53% 31.14% 7.49% 25.63% 52.70%
## 446: 19.65% 63.53% 31.14% 7.18% 25.93% 52.70%
## 447: 19.61% 63.53% 30.93% 7.18% 25.93% 52.70%
## 448: 19.61% 63.53% 30.72% 7.26% 25.93% 52.70%
## 449: 19.77% 63.53% 31.36% 7.41% 25.78% 52.70%
## 450: 19.69% 63.53% 30.72% 7.26% 26.23% 52.70%
## 451: 19.77% 63.53% 30.93% 7.33% 26.23% 52.70%
## 452: 19.69% 63.53% 30.72% 7.33% 26.08% 52.70%
## 453: 19.73% 63.53% 31.14% 7.26% 26.08% 52.70%
## 454: 19.73% 63.53% 31.14% 7.41% 25.78% 52.70%
## 455: 19.65% 63.53% 31.36% 7.26% 25.63% 52.70%
## 456: 19.77% 63.53% 31.36% 7.33% 25.93% 52.70%
## 457: 19.69% 63.53% 31.36% 7.26% 25.78% 52.70%
## 458: 19.81% 63.53% 31.36% 7.33% 26.08% 52.70%
## 459: 19.88% 63.53% 31.36% 7.41% 26.23% 52.70%
## 460: 19.96% 63.53% 31.57% 7.49% 26.23% 52.70%
## 461: 19.88% 63.53% 31.57% 7.49% 25.93% 52.70%
## 462: 19.84% 63.53% 31.36% 7.49% 25.93% 52.70%
## 463: 19.77% 63.53% 30.93% 7.41% 26.08% 52.70%
## 464: 19.88% 63.53% 30.93% 7.81% 25.78% 52.70%
## 465: 19.65% 63.53% 30.51% 7.57% 25.63% 52.70%
## 466: 19.81% 63.53% 30.72% 7.65% 25.93% 52.70%
## 467: 19.84% 63.53% 30.93% 7.65% 25.93% 52.70%
## 468: 19.88% 63.53% 31.14% 7.65% 25.93% 52.70%
## 469: 19.77% 62.35% 31.57% 7.41% 25.78% 52.70%
## 470: 19.73% 62.35% 31.14% 7.41% 25.93% 52.70%
## 471: 19.77% 62.35% 30.93% 7.57% 25.93% 52.70%
## 472: 19.81% 62.35% 31.36% 7.57% 25.78% 52.70%
## 473: 19.77% 62.35% 31.57% 7.33% 25.93% 52.70%
## 474: 19.77% 62.35% 31.36% 7.41% 25.93% 52.70%
## 475: 19.77% 62.35% 31.14% 7.57% 25.78% 52.70%
## 476: 19.65% 62.35% 30.93% 7.57% 25.48% 52.70%
## 477: 19.77% 62.35% 31.14% 7.49% 25.93% 52.70%
## 478: 19.73% 62.35% 31.36% 7.41% 25.78% 52.70%
## 479: 19.69% 62.35% 31.36% 7.41% 25.63% 52.70%
## 480: 19.69% 62.35% 31.36% 7.49% 25.48% 52.70%
## 481: 19.77% 62.35% 31.36% 7.49% 25.78% 52.70%
## 482: 19.77% 62.35% 31.14% 7.49% 25.93% 52.70%
## 483: 19.69% 62.35% 31.14% 7.49% 25.63% 52.70%
## 484: 19.77% 62.35% 31.36% 7.49% 25.78% 52.70%
## 485: 19.81% 62.35% 31.36% 7.65% 25.63% 52.70%
## 486: 19.96% 62.35% 31.78% 7.65% 25.93% 52.70%
## 487: 19.96% 62.35% 31.57% 7.73% 25.93% 52.70%
## 488: 19.84% 62.35% 31.57% 7.49% 25.93% 52.70%
## 489: 19.88% 62.35% 31.36% 7.57% 26.08% 52.70%
## 490: 19.88% 62.35% 31.57% 7.49% 26.08% 52.70%
## 491: 19.96% 62.35% 31.78% 7.41% 26.38% 52.70%
## 492: 20.04% 62.35% 31.57% 7.49% 26.68% 52.70%
## 493: 20.00% 62.35% 31.57% 7.57% 26.38% 52.70%
## 494: 20.12% 62.35% 31.57% 7.65% 26.68% 52.70%
## 495: 19.92% 62.35% 31.36% 7.49% 26.38% 52.70%
## 496: 19.84% 62.35% 31.14% 7.57% 26.08% 52.70%
## 497: 19.77% 62.35% 31.14% 7.49% 25.93% 52.70%
## 498: 20.00% 62.35% 31.36% 7.73% 26.23% 52.70%
## 499: 19.96% 62.35% 31.57% 7.73% 25.93% 52.70%
## 500: 20.00% 62.35% 31.57% 7.73% 26.08% 52.70%
## 501: 19.92% 62.35% 31.36% 7.65% 26.08% 52.70%
## 502: 19.96% 62.35% 31.57% 7.49% 26.38% 52.70%
## 503: 19.96% 62.35% 31.57% 7.65% 26.08% 52.70%
## 504: 19.88% 62.35% 31.14% 7.65% 26.08% 52.70%
## 505: 19.96% 62.35% 31.36% 7.73% 26.08% 52.70%
## 506: 20.00% 62.35% 31.36% 7.65% 26.38% 52.70%
## 507: 19.92% 62.35% 31.36% 7.65% 26.08% 52.70%
## 508: 19.92% 61.18% 31.36% 7.73% 26.08% 52.70%
## 509: 20.00% 62.35% 31.36% 7.73% 26.23% 52.70%
## 510: 19.92% 62.35% 31.14% 7.73% 26.08% 52.70%
## 511: 19.88% 62.35% 31.14% 7.57% 26.23% 52.70%
## 512: 19.81% 61.18% 31.36% 7.57% 25.93% 52.70%
## 513: 19.61% 61.18% 30.93% 7.33% 25.93% 52.70%
## 514: 19.65% 61.18% 30.72% 7.49% 25.93% 52.70%
## 515: 19.69% 61.18% 31.14% 7.49% 25.78% 52.70%
## 516: 19.84% 61.18% 31.36% 7.57% 26.08% 52.70%
## 517: 19.73% 61.18% 31.36% 7.49% 25.78% 52.70%
## 518: 19.77% 61.18% 30.93% 7.57% 26.08% 52.70%
## 519: 19.84% 61.18% 30.93% 7.49% 26.53% 52.70%
## 520: 20.00% 61.18% 31.36% 7.73% 26.38% 52.70%
## 521: 19.88% 61.18% 30.93% 7.73% 26.23% 52.70%
## 522: 19.92% 61.18% 30.93% 7.89% 26.08% 52.70%
## 523: 19.96% 61.18% 30.93% 7.81% 26.38% 52.70%
## 524: 19.92% 61.18% 31.14% 7.81% 26.08% 52.70%
## 525: 19.81% 61.18% 30.93% 7.73% 25.93% 52.70%
## 526: 19.88% 61.18% 31.14% 7.65% 26.23% 52.70%
## 527: 19.92% 61.18% 31.36% 7.73% 26.08% 52.70%
## 528: 19.84% 61.18% 30.93% 7.65% 26.23% 52.70%
## 529: 19.81% 61.18% 31.36% 7.49% 26.08% 52.70%
## 530: 19.65% 61.18% 30.51% 7.49% 26.08% 52.70%
## 531: 19.77% 61.18% 30.72% 7.65% 26.08% 52.70%
## 532: 19.53% 61.18% 30.08% 7.41% 26.08% 52.70%
## 533: 19.61% 61.18% 30.30% 7.49% 26.08% 52.70%
## 534: 19.65% 61.18% 30.93% 7.41% 25.93% 52.70%
## 535: 19.69% 61.18% 30.93% 7.41% 26.08% 52.70%
## 536: 19.69% 61.18% 30.93% 7.26% 26.23% 54.05%
## 537: 19.61% 61.18% 30.51% 7.33% 26.23% 52.70%
## 538: 19.77% 61.18% 30.72% 7.57% 26.08% 54.05%
## 539: 19.73% 61.18% 30.93% 7.49% 25.93% 54.05%
## 540: 19.61% 61.18% 30.51% 7.41% 25.93% 54.05%
## 541: 19.69% 61.18% 30.93% 7.41% 25.93% 54.05%
## 542: 19.73% 61.18% 30.72% 7.57% 25.93% 54.05%
## 543: 19.77% 61.18% 31.14% 7.41% 26.08% 54.05%
## 544: 19.69% 61.18% 30.51% 7.41% 26.23% 54.05%
## 545: 19.65% 61.18% 30.72% 7.33% 26.08% 54.05%
## 546: 19.61% 61.18% 30.72% 7.33% 25.93% 54.05%
## 547: 19.65% 61.18% 30.72% 7.41% 25.93% 54.05%
## 548: 19.65% 61.18% 30.30% 7.57% 25.93% 54.05%
## 549: 19.49% 61.18% 30.72% 7.33% 25.48% 54.05%
## 550: 19.49% 61.18% 30.72% 7.33% 25.48% 54.05%
## 551: 19.61% 61.18% 30.72% 7.49% 25.63% 54.05%
## 552: 19.65% 61.18% 30.72% 7.57% 25.63% 54.05%
## 553: 19.77% 61.18% 30.93% 7.57% 25.93% 54.05%
## 554: 19.73% 61.18% 30.93% 7.57% 25.78% 54.05%
## 555: 19.73% 61.18% 30.72% 7.65% 25.78% 54.05%
## 556: 19.65% 61.18% 30.93% 7.65% 25.48% 52.70%
## 557: 19.61% 61.18% 30.93% 7.41% 25.63% 54.05%
## 558: 19.69% 61.18% 30.72% 7.57% 25.93% 52.70%
## 559: 19.69% 61.18% 30.93% 7.57% 25.78% 52.70%
## 560: 19.65% 61.18% 31.36% 7.41% 25.63% 52.70%
## 561: 19.69% 61.18% 31.14% 7.49% 25.78% 52.70%
## 562: 19.53% 61.18% 30.93% 7.41% 25.48% 52.70%
## 563: 19.57% 61.18% 31.14% 7.41% 25.48% 52.70%
## 564: 19.65% 61.18% 31.36% 7.41% 25.63% 52.70%
## 565: 19.57% 61.18% 30.72% 7.57% 25.48% 52.70%
## 566: 19.57% 61.18% 31.14% 7.49% 25.34% 52.70%
## 567: 19.53% 61.18% 31.14% 7.49% 25.34% 51.35%
## 568: 19.46% 61.18% 30.93% 7.49% 25.19% 51.35%
## 569: 19.42% 61.18% 30.51% 7.57% 25.04% 52.70%
## 570: 19.61% 61.18% 30.72% 7.57% 25.48% 54.05%
## 571: 19.61% 60.00% 30.93% 7.57% 25.48% 54.05%
## 572: 19.38% 60.00% 30.51% 7.41% 25.34% 52.70%
## 573: 19.61% 60.00% 30.93% 7.49% 25.78% 52.70%
## 574: 19.46% 60.00% 30.72% 7.33% 25.63% 52.70%
## 575: 19.49% 60.00% 30.93% 7.41% 25.48% 52.70%
## 576: 19.46% 60.00% 30.72% 7.41% 25.34% 54.05%
## 577: 19.46% 60.00% 30.72% 7.41% 25.48% 52.70%
## 578: 19.46% 60.00% 30.72% 7.41% 25.34% 54.05%
## 579: 19.46% 60.00% 30.51% 7.49% 25.34% 54.05%
## 580: 19.26% 60.00% 30.51% 7.26% 25.04% 54.05%
## 581: 19.26% 60.00% 30.51% 7.26% 25.04% 54.05%
## 582: 19.34% 60.00% 30.51% 7.02% 25.78% 54.05%
## 583: 19.53% 60.00% 30.72% 7.33% 25.78% 54.05%
## 584: 19.38% 60.00% 30.51% 7.10% 25.78% 54.05%
## 585: 19.34% 60.00% 30.30% 7.26% 25.48% 54.05%
## 586: 19.38% 60.00% 30.51% 7.18% 25.78% 52.70%
## 587: 19.22% 60.00% 30.08% 7.18% 25.48% 52.70%
## 588: 19.34% 60.00% 30.30% 7.26% 25.63% 52.70%
## 589: 19.30% 61.18% 30.30% 7.18% 25.48% 52.70%
## 590: 19.42% 60.00% 30.51% 7.26% 25.78% 52.70%
## 591: 19.42% 60.00% 30.30% 7.33% 25.78% 52.70%
## 592: 19.30% 60.00% 30.30% 7.18% 25.63% 52.70%
## 593: 19.38% 60.00% 30.30% 7.26% 25.78% 52.70%
## 594: 19.38% 60.00% 30.51% 7.26% 25.63% 52.70%
## 595: 19.46% 60.00% 30.51% 7.33% 25.78% 52.70%
## 596: 19.38% 60.00% 30.51% 7.26% 25.63% 52.70%
## 597: 19.30% 60.00% 30.51% 7.18% 25.48% 52.70%
## 598: 19.38% 60.00% 30.30% 7.26% 25.78% 52.70%
## 599: 19.30% 60.00% 30.51% 7.18% 25.48% 52.70%
## 600: 19.26% 60.00% 30.30% 7.18% 25.48% 52.70%
## 601: 19.30% 60.00% 30.51% 7.10% 25.63% 52.70%
## 602: 19.46% 60.00% 30.51% 7.26% 25.78% 54.05%
## 603: 19.49% 60.00% 30.93% 7.18% 25.78% 54.05%
## 604: 19.46% 60.00% 30.51% 7.18% 25.93% 54.05%
## 605: 19.42% 60.00% 30.51% 7.18% 25.78% 54.05%
## 606: 19.46% 60.00% 30.72% 7.18% 25.78% 54.05%
## 607: 19.42% 60.00% 30.51% 7.18% 25.78% 54.05%
## 608: 19.42% 60.00% 30.72% 7.26% 25.48% 54.05%
## 609: 19.46% 60.00% 30.93% 7.10% 25.78% 54.05%
## 610: 19.53% 60.00% 30.72% 7.18% 25.93% 55.41%
## 611: 19.49% 60.00% 30.72% 7.18% 25.78% 55.41%
## 612: 19.46% 60.00% 30.51% 7.18% 25.78% 55.41%
## 613: 19.61% 61.18% 30.72% 7.18% 26.08% 55.41%
## 614: 19.38% 60.00% 30.51% 7.18% 25.63% 54.05%
## 615: 19.42% 60.00% 30.72% 7.10% 25.78% 54.05%
## 616: 19.46% 60.00% 30.72% 7.18% 25.78% 54.05%
## 617: 19.42% 60.00% 30.51% 7.26% 25.63% 54.05%
## 618: 19.53% 60.00% 30.72% 7.26% 25.78% 55.41%
## 619: 19.42% 60.00% 30.51% 7.18% 25.78% 54.05%
## 620: 19.49% 60.00% 30.93% 7.18% 25.63% 55.41%
## 621: 19.57% 60.00% 30.93% 7.10% 26.08% 55.41%
## 622: 19.53% 60.00% 30.93% 7.18% 25.78% 55.41%
## 623: 19.42% 60.00% 30.72% 7.02% 25.78% 55.41%
## 624: 19.53% 60.00% 30.93% 7.18% 25.78% 55.41%
## 625: 19.61% 60.00% 30.93% 7.33% 25.78% 55.41%
## 626: 19.57% 60.00% 30.51% 7.26% 26.08% 55.41%
## 627: 19.57% 60.00% 30.51% 7.26% 26.08% 55.41%
## 628: 19.65% 60.00% 30.72% 7.18% 26.38% 55.41%
## 629: 19.73% 60.00% 30.93% 7.26% 26.38% 55.41%
## 630: 19.81% 60.00% 30.93% 7.33% 26.53% 55.41%
## 631: 19.65% 60.00% 30.72% 7.33% 26.08% 55.41%
## 632: 19.65% 60.00% 30.93% 7.18% 26.23% 55.41%
## 633: 19.61% 60.00% 30.93% 7.18% 26.08% 55.41%
## 634: 19.53% 60.00% 30.72% 7.18% 25.93% 55.41%
## 635: 19.65% 60.00% 30.93% 7.18% 26.23% 55.41%
## 636: 19.65% 60.00% 31.14% 7.18% 26.08% 55.41%
## 637: 19.69% 61.18% 31.36% 7.18% 25.93% 55.41%
## 638: 19.77% 61.18% 31.57% 7.18% 26.08% 55.41%
## 639: 19.77% 61.18% 31.36% 7.26% 26.08% 55.41%
## 640: 19.77% 61.18% 31.14% 7.33% 26.08% 55.41%
## 641: 19.77% 61.18% 31.36% 7.26% 26.08% 55.41%
## 642: 19.81% 61.18% 31.14% 7.26% 26.38% 55.41%
## 643: 19.81% 61.18% 30.93% 7.26% 26.53% 55.41%
## 644: 19.84% 61.18% 31.14% 7.26% 26.53% 55.41%
## 645: 19.77% 61.18% 30.93% 7.18% 26.53% 55.41%
## 646: 19.81% 61.18% 31.14% 7.18% 26.53% 55.41%
## 647: 19.81% 61.18% 31.36% 7.10% 26.53% 55.41%
## 648: 19.92% 62.35% 31.36% 7.18% 26.68% 55.41%
## 649: 19.88% 62.35% 31.36% 7.10% 26.68% 55.41%
## 650: 19.92% 61.18% 31.57% 7.18% 26.68% 55.41%
## 651: 19.88% 61.18% 31.78% 7.10% 26.53% 55.41%
## 652: 19.77% 61.18% 31.14% 7.02% 26.68% 55.41%
## 653: 19.81% 61.18% 31.36% 7.02% 26.68% 55.41%
## 654: 19.65% 61.18% 30.93% 7.02% 26.53% 54.05%
## 655: 19.61% 60.00% 30.93% 7.02% 26.53% 54.05%
## 656: 19.81% 61.18% 31.57% 7.02% 26.53% 55.41%
## 657: 19.77% 61.18% 31.14% 7.02% 26.68% 55.41%
## 658: 19.88% 61.18% 31.57% 7.10% 26.68% 55.41%
## 659: 19.73% 60.00% 31.14% 7.10% 26.53% 55.41%
## 660: 19.81% 61.18% 31.36% 7.10% 26.68% 54.05%
## 661: 19.73% 61.18% 31.36% 7.02% 26.53% 54.05%
## 662: 19.65% 61.18% 31.14% 7.02% 26.53% 52.70%
## 663: 19.61% 61.18% 31.14% 7.10% 26.08% 54.05%
## 664: 19.53% 61.18% 31.14% 6.94% 26.23% 52.70%
## 665: 19.65% 61.18% 31.36% 7.02% 26.38% 52.70%
## 666: 19.73% 61.18% 31.57% 7.10% 26.23% 54.05%
## 667: 19.69% 61.18% 31.57% 6.94% 26.38% 54.05%
## 668: 19.53% 61.18% 31.14% 6.94% 26.08% 54.05%
## 669: 19.69% 61.18% 31.57% 6.94% 26.38% 54.05%
## 670: 19.53% 61.18% 31.14% 6.94% 26.08% 54.05%
## 671: 19.65% 61.18% 31.57% 6.86% 26.38% 54.05%
## 672: 19.73% 61.18% 31.78% 6.94% 26.38% 54.05%
## 673: 19.69% 61.18% 31.57% 6.86% 26.53% 54.05%
## 674: 19.69% 62.35% 31.57% 6.86% 26.38% 54.05%
## 675: 19.69% 62.35% 31.57% 6.86% 26.38% 54.05%
## 676: 19.65% 62.35% 31.14% 6.86% 26.53% 54.05%
## 677: 19.57% 62.35% 31.14% 6.78% 26.38% 54.05%
## 678: 19.61% 61.18% 30.93% 6.94% 26.53% 54.05%
## 679: 19.73% 62.35% 31.36% 6.94% 26.53% 54.05%
## 680: 19.69% 61.18% 31.14% 7.02% 26.53% 54.05%
## 681: 19.57% 61.18% 30.93% 7.02% 26.38% 52.70%
## 682: 19.77% 61.18% 31.36% 7.02% 26.68% 54.05%
## 683: 19.65% 61.18% 31.36% 6.86% 26.53% 54.05%
## 684: 19.65% 61.18% 31.14% 7.02% 26.38% 54.05%
## 685: 19.61% 61.18% 31.36% 6.94% 26.23% 54.05%
## 686: 19.53% 61.18% 30.93% 6.86% 26.53% 52.70%
## 687: 19.57% 61.18% 31.14% 6.86% 26.53% 52.70%
## 688: 19.57% 61.18% 30.93% 6.94% 26.53% 52.70%
## 689: 19.57% 61.18% 31.14% 6.86% 26.53% 52.70%
## 690: 19.61% 61.18% 31.36% 6.86% 26.53% 52.70%
## 691: 19.53% 60.00% 30.93% 6.94% 26.53% 52.70%
## 692: 19.61% 61.18% 31.36% 6.86% 26.53% 52.70%
## 693: 19.53% 60.00% 31.14% 6.86% 26.53% 52.70%
## 694: 19.53% 60.00% 31.36% 6.86% 26.38% 52.70%
## 695: 19.61% 61.18% 31.36% 6.86% 26.53% 52.70%
## 696: 19.57% 61.18% 31.36% 6.78% 26.53% 52.70%
## 697: 19.61% 61.18% 31.57% 6.86% 26.38% 52.70%
## 698: 19.61% 61.18% 31.36% 6.86% 26.53% 52.70%
## 699: 19.49% 60.00% 31.14% 6.78% 26.53% 52.70%
## 700: 19.42% 60.00% 30.93% 6.70% 26.53% 52.70%
## 701: 19.53% 60.00% 31.36% 6.78% 26.53% 52.70%
## 702: 19.42% 60.00% 30.93% 6.70% 26.53% 52.70%
## 703: 19.49% 60.00% 31.36% 6.78% 26.38% 52.70%
## 704: 19.42% 60.00% 30.93% 6.70% 26.53% 52.70%
## 705: 19.57% 60.00% 31.36% 6.86% 26.53% 52.70%
## 706: 19.57% 60.00% 30.93% 6.94% 26.68% 52.70%
## 707: 19.49% 60.00% 30.93% 6.86% 26.53% 52.70%
## 708: 19.38% 60.00% 30.93% 6.86% 26.08% 52.70%
## 709: 19.57% 60.00% 31.36% 6.86% 26.53% 52.70%
## 710: 19.42% 60.00% 31.14% 6.70% 26.38% 52.70%
## 711: 19.34% 60.00% 30.93% 6.78% 26.08% 52.70%
## 712: 19.30% 61.18% 30.72% 6.70% 26.08% 52.70%
## 713: 19.42% 61.18% 31.14% 6.78% 26.08% 52.70%
## 714: 19.38% 61.18% 31.14% 6.62% 26.23% 52.70%
## 715: 19.18% 60.00% 30.72% 6.62% 25.93% 52.70%
## 716: 19.30% 61.18% 30.72% 6.78% 25.93% 52.70%
## 717: 19.34% 60.00% 31.14% 6.78% 25.93% 52.70%
## 718: 19.38% 60.00% 30.93% 7.02% 25.78% 52.70%
## 719: 19.34% 60.00% 30.93% 6.86% 25.93% 52.70%
## 720: 19.46% 60.00% 30.93% 7.02% 26.08% 52.70%
## 721: 19.46% 60.00% 31.14% 6.94% 26.08% 52.70%
## 722: 19.57% 61.18% 31.14% 7.10% 26.08% 52.70%
## 723: 19.46% 60.00% 30.72% 7.02% 26.23% 52.70%
## 724: 19.53% 60.00% 30.93% 7.10% 26.23% 52.70%
## 725: 19.53% 60.00% 30.93% 7.10% 26.23% 52.70%
## 726: 19.61% 60.00% 31.14% 7.10% 26.38% 52.70%
## 727: 19.53% 60.00% 30.93% 7.02% 26.38% 52.70%
## 728: 19.49% 60.00% 30.93% 7.02% 26.23% 52.70%
## 729: 19.53% 60.00% 30.93% 7.10% 26.23% 52.70%
## 730: 19.49% 60.00% 30.93% 7.02% 26.23% 52.70%
## 731: 19.49% 60.00% 30.93% 7.10% 26.08% 52.70%
## 732: 19.42% 60.00% 30.72% 7.02% 26.08% 52.70%
## 733: 19.34% 60.00% 30.51% 6.94% 26.08% 52.70%
## 734: 19.49% 60.00% 30.93% 7.10% 26.08% 52.70%
## 735: 19.46% 60.00% 30.93% 6.94% 26.23% 52.70%
## 736: 19.42% 60.00% 30.93% 6.86% 26.23% 52.70%
## 737: 19.34% 60.00% 30.72% 6.86% 26.08% 52.70%
## 738: 19.53% 60.00% 31.36% 7.02% 26.08% 52.70%
## 739: 19.42% 60.00% 30.93% 6.94% 26.08% 52.70%
## 740: 19.46% 60.00% 31.36% 6.94% 25.93% 52.70%
## 741: 19.38% 60.00% 30.93% 6.86% 26.08% 52.70%
## 742: 19.42% 60.00% 30.93% 6.94% 26.08% 52.70%
## 743: 19.46% 60.00% 31.36% 6.78% 26.23% 52.70%
## 744: 19.53% 60.00% 31.36% 6.86% 26.38% 52.70%
## 745: 19.38% 60.00% 30.93% 6.86% 26.08% 52.70%
## 746: 19.38% 60.00% 31.14% 6.86% 25.93% 52.70%
## 747: 19.46% 60.00% 31.14% 6.86% 26.23% 52.70%
## 748: 19.42% 60.00% 31.14% 6.78% 26.23% 52.70%
## 749: 19.42% 60.00% 30.93% 6.86% 26.23% 52.70%
## 750: 19.46% 60.00% 31.14% 6.86% 26.23% 52.70%
## 751: 19.38% 60.00% 31.14% 6.78% 26.08% 52.70%
## 752: 19.46% 60.00% 31.14% 6.86% 26.23% 52.70%
## 753: 19.49% 60.00% 31.14% 6.94% 26.23% 52.70%
## 754: 19.34% 60.00% 30.72% 6.78% 26.23% 52.70%
## 755: 19.34% 60.00% 30.72% 6.78% 26.23% 52.70%
## 756: 19.38% 60.00% 30.72% 6.78% 26.38% 52.70%
## 757: 19.30% 60.00% 30.51% 6.70% 26.38% 52.70%
## 758: 19.49% 60.00% 31.14% 6.86% 26.38% 52.70%
## 759: 19.30% 60.00% 30.72% 6.78% 26.08% 52.70%
## 760: 19.34% 60.00% 30.72% 6.78% 26.23% 52.70%
## 761: 19.26% 60.00% 30.72% 6.70% 26.08% 52.70%
## 762: 19.34% 60.00% 30.72% 6.86% 26.08% 52.70%
## 763: 19.30% 60.00% 30.93% 6.70% 26.08% 52.70%
## 764: 19.26% 60.00% 30.93% 6.70% 25.93% 52.70%
## 765: 19.26% 60.00% 30.72% 6.70% 26.08% 52.70%
## 766: 19.26% 60.00% 30.72% 6.62% 26.23% 52.70%
## 767: 19.22% 60.00% 30.72% 6.62% 26.08% 52.70%
## 768: 19.34% 60.00% 30.72% 6.62% 26.53% 52.70%
## 769: 19.30% 60.00% 30.72% 6.62% 26.38% 52.70%
## 770: 19.26% 60.00% 30.30% 6.62% 26.53% 52.70%
## 771: 19.34% 60.00% 30.51% 6.70% 26.53% 52.70%
## 772: 19.30% 60.00% 30.72% 6.70% 26.23% 52.70%
## 773: 19.18% 60.00% 30.30% 6.70% 26.08% 52.70%
## 774: 19.22% 60.00% 30.30% 6.62% 26.38% 52.70%
## 775: 19.34% 60.00% 30.93% 6.70% 26.23% 52.70%
## 776: 19.18% 60.00% 30.72% 6.70% 25.78% 52.70%
## 777: 19.30% 60.00% 30.72% 6.78% 26.08% 52.70%
## 778: 19.26% 60.00% 30.51% 6.78% 26.08% 52.70%
## 779: 19.18% 60.00% 30.72% 6.78% 25.63% 52.70%
## 780: 19.18% 60.00% 30.51% 6.78% 25.78% 52.70%
## 781: 19.26% 60.00% 30.72% 6.70% 26.08% 52.70%
## 782: 19.22% 60.00% 30.51% 6.70% 26.08% 52.70%
## 783: 19.26% 60.00% 30.72% 6.70% 26.08% 52.70%
## 784: 19.30% 60.00% 30.72% 6.86% 25.93% 52.70%
## 785: 19.30% 60.00% 30.72% 6.78% 26.08% 52.70%
## 786: 19.30% 60.00% 30.93% 6.62% 26.23% 52.70%
## 787: 19.30% 60.00% 30.72% 6.70% 26.23% 52.70%
## 788: 19.11% 60.00% 30.30% 6.62% 25.93% 52.70%
## 789: 19.14% 60.00% 30.30% 6.70% 25.93% 52.70%
## 790: 19.14% 60.00% 30.30% 6.70% 25.93% 52.70%
## 791: 19.18% 60.00% 30.51% 6.70% 25.93% 52.70%
## 792: 19.14% 60.00% 30.30% 6.70% 25.93% 52.70%
## 793: 19.14% 60.00% 30.51% 6.70% 25.78% 52.70%
## 794: 19.14% 60.00% 30.51% 6.70% 25.78% 52.70%
## 795: 19.14% 60.00% 30.51% 6.62% 25.93% 52.70%
## 796: 19.14% 60.00% 30.30% 6.62% 26.08% 52.70%
## 797: 19.22% 60.00% 30.08% 6.94% 25.93% 52.70%
## 798: 19.11% 60.00% 30.08% 6.70% 25.93% 52.70%
## 799: 19.18% 60.00% 30.08% 6.86% 25.93% 52.70%
## 800: 19.42% 60.00% 30.30% 7.10% 26.23% 52.70%
## 801: 19.34% 60.00% 30.30% 6.94% 26.23% 52.70%
## 802: 19.26% 60.00% 30.30% 6.86% 26.08% 52.70%
## 803: 19.34% 60.00% 30.30% 6.86% 26.38% 52.70%
## 804: 19.22% 60.00% 30.08% 6.78% 26.23% 52.70%
## 805: 19.26% 60.00% 30.08% 6.78% 26.38% 52.70%
## 806: 19.30% 60.00% 30.30% 6.86% 26.23% 52.70%
## 807: 19.34% 60.00% 30.51% 6.86% 26.23% 52.70%
## 808: 19.34% 60.00% 30.51% 6.94% 26.08% 52.70%
## 809: 19.34% 60.00% 30.51% 6.94% 26.08% 52.70%
## 810: 19.30% 60.00% 30.51% 6.86% 26.08% 52.70%
## 811: 19.26% 60.00% 30.30% 6.94% 25.93% 52.70%
## 812: 19.26% 60.00% 30.30% 6.86% 26.08% 52.70%
## 813: 19.26% 60.00% 30.30% 6.78% 26.23% 52.70%
## 814: 19.22% 60.00% 30.30% 6.78% 26.08% 52.70%
## 815: 19.22% 60.00% 30.30% 6.78% 26.08% 52.70%
## 816: 19.22% 60.00% 30.30% 6.78% 26.08% 52.70%
## 817: 19.30% 60.00% 30.30% 7.02% 25.93% 52.70%
## 818: 19.38% 61.18% 30.51% 7.02% 25.93% 52.70%
## 819: 19.34% 60.00% 30.51% 6.94% 26.08% 52.70%
## 820: 19.38% 60.00% 30.51% 7.02% 26.08% 52.70%
## 821: 19.30% 60.00% 30.51% 6.94% 25.93% 52.70%
## 822: 19.38% 60.00% 30.51% 6.94% 26.23% 52.70%
## 823: 19.22% 60.00% 30.30% 6.70% 26.23% 52.70%
## 824: 19.30% 60.00% 30.30% 6.78% 26.38% 52.70%
## 825: 19.26% 60.00% 30.51% 6.70% 26.23% 52.70%
## 826: 19.34% 60.00% 30.30% 6.94% 26.23% 52.70%
## 827: 19.34% 60.00% 30.30% 6.94% 26.23% 52.70%
## 828: 19.34% 60.00% 30.08% 6.94% 26.38% 52.70%
## 829: 19.38% 60.00% 30.51% 6.94% 26.23% 52.70%
## 830: 19.26% 60.00% 30.08% 6.94% 26.08% 52.70%
## 831: 19.22% 60.00% 30.08% 6.94% 25.93% 52.70%
## 832: 19.26% 60.00% 30.30% 6.94% 25.93% 52.70%
## 833: 19.26% 60.00% 30.30% 6.94% 25.93% 52.70%
## 834: 19.34% 60.00% 30.51% 6.94% 26.08% 52.70%
## 835: 19.30% 60.00% 30.51% 6.78% 26.23% 52.70%
## 836: 19.22% 60.00% 30.30% 6.70% 26.23% 52.70%
## 837: 19.22% 60.00% 30.30% 6.86% 25.93% 52.70%
## 838: 19.22% 60.00% 30.30% 6.86% 25.93% 52.70%
## 839: 19.26% 60.00% 30.30% 6.78% 26.23% 52.70%
## 840: 19.18% 60.00% 30.30% 6.78% 25.93% 52.70%
## 841: 19.18% 60.00% 30.30% 6.70% 26.08% 52.70%
## 842: 19.14% 60.00% 30.08% 6.78% 25.93% 52.70%
## 843: 19.14% 60.00% 30.08% 6.86% 25.78% 52.70%
## 844: 19.18% 60.00% 29.87% 6.94% 25.93% 52.70%
## 845: 19.30% 60.00% 30.51% 6.86% 26.08% 52.70%
## 846: 19.26% 60.00% 30.30% 6.94% 25.93% 52.70%
## 847: 19.30% 60.00% 30.30% 6.94% 26.08% 52.70%
## 848: 19.34% 60.00% 30.51% 6.94% 26.08% 52.70%
## 849: 19.26% 60.00% 30.30% 6.86% 26.08% 52.70%
## 850: 19.30% 60.00% 30.51% 6.94% 25.93% 52.70%
## 851: 19.30% 60.00% 30.30% 6.94% 26.08% 52.70%
## 852: 19.22% 60.00% 30.08% 6.94% 25.93% 52.70%
## 853: 19.26% 60.00% 30.30% 6.94% 25.93% 52.70%
## 854: 19.26% 60.00% 30.08% 6.94% 26.08% 52.70%
## 855: 19.26% 60.00% 30.08% 6.94% 26.08% 52.70%
## 856: 19.26% 60.00% 30.08% 6.94% 26.08% 52.70%
## 857: 19.26% 60.00% 29.87% 6.94% 26.23% 52.70%
## 858: 19.22% 60.00% 29.87% 6.86% 26.23% 52.70%
## 859: 19.18% 60.00% 29.87% 6.86% 26.08% 52.70%
## 860: 19.18% 60.00% 29.87% 6.86% 26.08% 52.70%
## 861: 19.22% 60.00% 30.30% 6.86% 25.93% 52.70%
## 862: 19.11% 60.00% 29.45% 6.86% 26.08% 52.70%
## 863: 19.18% 60.00% 29.66% 6.86% 26.23% 52.70%
## 864: 19.14% 60.00% 29.66% 6.86% 26.08% 52.70%
## 865: 19.18% 60.00% 30.08% 6.94% 25.78% 52.70%
## 866: 19.26% 60.00% 30.08% 6.78% 26.38% 52.70%
## 867: 19.11% 61.18% 29.87% 6.70% 25.93% 52.70%
## 868: 19.18% 60.00% 30.08% 6.70% 26.23% 52.70%
## 869: 19.18% 60.00% 30.08% 6.70% 26.23% 52.70%
## 870: 19.11% 60.00% 29.87% 6.70% 26.08% 52.70%
## 871: 19.03% 60.00% 29.66% 6.70% 25.93% 52.70%
## 872: 19.14% 60.00% 29.87% 6.78% 26.08% 52.70%
## 873: 19.11% 60.00% 29.87% 6.78% 25.93% 52.70%
## 874: 19.22% 61.18% 29.87% 6.78% 26.23% 52.70%
## 875: 19.07% 61.18% 29.66% 6.70% 25.93% 52.70%
## 876: 19.18% 61.18% 29.87% 6.78% 25.93% 54.05%
## 877: 19.26% 61.18% 29.87% 6.78% 26.23% 54.05%
## 878: 19.26% 61.18% 30.30% 6.78% 25.93% 54.05%
## 879: 19.22% 61.18% 30.08% 6.78% 25.93% 54.05%
## 880: 19.22% 61.18% 29.87% 6.70% 26.23% 54.05%
## 881: 19.30% 61.18% 30.30% 6.70% 26.23% 54.05%
## 882: 19.18% 61.18% 29.87% 6.78% 25.93% 54.05%
## 883: 19.26% 61.18% 30.08% 6.70% 26.23% 54.05%
## 884: 19.18% 61.18% 30.51% 6.70% 25.78% 52.70%
## 885: 19.18% 61.18% 30.30% 6.70% 25.93% 52.70%
## 886: 19.11% 61.18% 30.08% 6.70% 25.78% 52.70%
## 887: 19.22% 61.18% 30.51% 6.70% 25.93% 52.70%
## 888: 19.22% 61.18% 30.30% 6.70% 25.93% 54.05%
## 889: 19.18% 61.18% 30.30% 6.70% 25.93% 52.70%
## 890: 19.18% 61.18% 30.30% 6.70% 25.93% 52.70%
## 891: 19.18% 61.18% 30.30% 6.62% 26.08% 52.70%
## 892: 19.14% 61.18% 30.08% 6.70% 25.93% 52.70%
## 893: 19.18% 61.18% 30.08% 6.78% 25.93% 52.70%
## 894: 19.11% 61.18% 30.30% 6.62% 25.78% 52.70%
## 895: 19.18% 61.18% 30.30% 6.70% 25.93% 52.70%
## 896: 19.18% 61.18% 30.30% 6.78% 25.78% 52.70%
## 897: 19.18% 61.18% 30.30% 6.78% 25.78% 52.70%
## 898: 19.18% 61.18% 30.30% 6.78% 25.78% 52.70%
## 899: 19.18% 61.18% 30.30% 6.70% 25.93% 52.70%
## 900: 19.14% 61.18% 30.30% 6.78% 25.63% 52.70%
## 901: 19.11% 61.18% 30.08% 6.78% 25.63% 52.70%
## 902: 19.14% 61.18% 30.08% 6.70% 25.93% 52.70%
## 903: 19.18% 61.18% 30.08% 6.78% 25.93% 52.70%
## 904: 19.07% 61.18% 30.08% 6.70% 25.63% 52.70%
## 905: 19.18% 61.18% 30.30% 6.78% 25.78% 52.70%
## 906: 19.22% 61.18% 30.30% 6.78% 25.93% 52.70%
## 907: 19.18% 61.18% 30.08% 6.78% 25.93% 52.70%
## 908: 19.11% 61.18% 30.30% 6.70% 25.63% 52.70%
## 909: 19.18% 61.18% 30.30% 6.70% 25.93% 52.70%
## 910: 19.18% 61.18% 30.30% 6.78% 25.78% 52.70%
## 911: 19.11% 61.18% 30.30% 6.62% 25.78% 52.70%
## 912: 19.07% 61.18% 30.08% 6.62% 25.78% 52.70%
## 913: 19.18% 61.18% 30.30% 6.70% 25.93% 52.70%
## 914: 19.07% 61.18% 30.08% 6.70% 25.63% 52.70%
## 915: 19.11% 61.18% 30.30% 6.62% 25.78% 52.70%
## 916: 19.07% 61.18% 30.08% 6.70% 25.63% 52.70%
## 917: 18.99% 61.18% 30.08% 6.55% 25.63% 52.70%
## 918: 19.07% 61.18% 30.30% 6.55% 25.78% 52.70%
## 919: 19.11% 62.35% 30.30% 6.55% 25.78% 52.70%
## 920: 19.11% 61.18% 30.30% 6.55% 25.93% 52.70%
## 921: 19.07% 61.18% 30.30% 6.55% 25.78% 52.70%
## 922: 19.03% 61.18% 30.30% 6.47% 25.78% 52.70%
## 923: 19.03% 61.18% 30.30% 6.47% 25.78% 52.70%
## 924: 18.99% 61.18% 30.30% 6.55% 25.48% 52.70%
## 925: 18.95% 61.18% 30.30% 6.55% 25.34% 52.70%
## 926: 18.99% 61.18% 30.30% 6.55% 25.48% 52.70%
## 927: 19.03% 61.18% 30.51% 6.55% 25.48% 52.70%
## 928: 19.03% 61.18% 30.51% 6.55% 25.48% 52.70%
## 929: 18.99% 61.18% 30.51% 6.55% 25.34% 52.70%
## 930: 18.91% 61.18% 30.30% 6.55% 25.19% 52.70%
## 931: 18.91% 61.18% 30.08% 6.62% 25.19% 52.70%
## 932: 18.91% 61.18% 30.30% 6.62% 25.04% 52.70%
## 933: 18.95% 61.18% 30.08% 6.62% 25.34% 52.70%
## 934: 19.03% 61.18% 30.30% 6.70% 25.34% 52.70%
## 935: 18.99% 61.18% 30.30% 6.62% 25.34% 52.70%
## 936: 18.99% 61.18% 30.51% 6.62% 25.19% 52.70%
## 937: 18.95% 61.18% 30.51% 6.62% 25.04% 52.70%
## 938: 19.03% 61.18% 30.51% 6.78% 25.04% 52.70%
## 939: 19.03% 61.18% 30.51% 6.70% 25.19% 52.70%
## 940: 19.18% 62.35% 30.72% 6.78% 25.34% 52.70%
## 941: 19.07% 62.35% 30.51% 6.70% 25.19% 52.70%
## 942: 19.14% 62.35% 30.51% 6.86% 25.19% 52.70%
## 943: 19.14% 62.35% 30.51% 6.70% 25.48% 52.70%
## 944: 19.18% 62.35% 30.72% 6.70% 25.48% 52.70%
## 945: 19.11% 62.35% 30.30% 6.78% 25.34% 52.70%
## 946: 19.07% 62.35% 30.51% 6.78% 25.04% 52.70%
## 947: 19.11% 62.35% 30.30% 6.86% 25.19% 52.70%
## 948: 19.22% 62.35% 30.51% 6.94% 25.34% 52.70%
## 949: 19.14% 62.35% 30.30% 6.86% 25.34% 52.70%
## 950: 19.26% 62.35% 30.30% 7.02% 25.48% 52.70%
## 951: 19.22% 62.35% 30.51% 6.94% 25.34% 52.70%
## 952: 19.14% 62.35% 30.51% 6.86% 25.19% 52.70%
## 953: 19.26% 62.35% 30.72% 6.94% 25.34% 52.70%
## 954: 19.18% 62.35% 30.51% 6.86% 25.34% 52.70%
## 955: 19.14% 62.35% 30.51% 6.86% 25.19% 52.70%
## 956: 19.18% 62.35% 30.72% 6.86% 25.19% 52.70%
## 957: 19.14% 62.35% 30.72% 6.86% 25.04% 52.70%
## 958: 19.22% 62.35% 30.93% 6.86% 25.19% 52.70%
## 959: 19.22% 62.35% 30.72% 6.94% 25.19% 52.70%
## 960: 19.26% 62.35% 30.93% 6.94% 25.19% 52.70%
## 961: 19.22% 62.35% 30.72% 6.94% 25.19% 52.70%
## 962: 19.26% 62.35% 30.72% 7.02% 25.19% 52.70%
## 963: 19.18% 62.35% 30.51% 7.02% 25.04% 52.70%
## 964: 19.14% 62.35% 30.51% 6.94% 25.04% 52.70%
## 965: 19.22% 62.35% 30.93% 6.86% 25.19% 52.70%
## 966: 19.22% 62.35% 30.72% 6.94% 25.19% 52.70%
## 967: 19.22% 62.35% 30.72% 6.94% 25.19% 52.70%
## 968: 19.14% 62.35% 30.72% 6.86% 25.04% 52.70%
## 969: 19.26% 62.35% 30.93% 6.86% 25.34% 52.70%
## 970: 19.26% 62.35% 30.51% 6.94% 25.48% 52.70%
## 971: 19.34% 62.35% 30.72% 7.02% 25.48% 52.70%
## 972: 19.42% 62.35% 30.72% 7.02% 25.78% 52.70%
## 973: 19.34% 62.35% 30.51% 7.02% 25.63% 52.70%
## 974: 19.34% 62.35% 30.93% 6.94% 25.48% 52.70%
## 975: 19.34% 62.35% 30.72% 6.94% 25.63% 52.70%
## 976: 19.34% 62.35% 30.93% 6.94% 25.48% 52.70%
## 977: 19.34% 62.35% 30.93% 6.94% 25.48% 52.70%
## 978: 19.38% 61.18% 30.72% 6.94% 25.93% 52.70%
## 979: 19.38% 62.35% 30.72% 6.86% 25.93% 52.70%
## 980: 19.34% 62.35% 30.72% 6.86% 25.78% 52.70%
## 981: 19.34% 61.18% 30.93% 6.86% 25.78% 52.70%
## 982: 19.34% 61.18% 30.93% 6.94% 25.63% 52.70%
## 983: 19.30% 61.18% 30.72% 6.94% 25.63% 52.70%
## 984: 19.30% 61.18% 30.72% 6.94% 25.63% 52.70%
## 985: 19.22% 61.18% 30.51% 6.86% 25.63% 52.70%
## 986: 19.22% 61.18% 30.51% 6.86% 25.63% 52.70%
## 987: 19.30% 61.18% 30.93% 6.78% 25.78% 52.70%
## 988: 19.38% 62.35% 30.72% 6.86% 25.93% 52.70%
## 989: 19.46% 62.35% 30.72% 6.94% 26.08% 52.70%
## 990: 19.26% 61.18% 30.72% 6.94% 25.48% 52.70%
## 991: 19.34% 61.18% 30.72% 6.94% 25.78% 52.70%
## 992: 19.26% 61.18% 30.93% 6.86% 25.48% 52.70%
## 993: 19.34% 62.35% 30.93% 6.86% 25.63% 52.70%
## 994: 19.30% 61.18% 31.14% 6.86% 25.48% 52.70%
## 995: 19.26% 61.18% 30.93% 6.86% 25.48% 52.70%
## 996: 19.38% 62.35% 30.93% 6.94% 25.63% 52.70%
## 997: 19.38% 61.18% 30.93% 6.94% 25.78% 52.70%
## 998: 19.38% 61.18% 31.14% 6.94% 25.63% 52.70%
## 999: 19.34% 61.18% 30.93% 6.94% 25.63% 52.70%
## 1000: 19.38% 61.18% 30.93% 6.94% 25.78% 52.70%
## 1001: 19.42% 62.35% 30.93% 6.94% 25.78% 52.70%
## 1002: 19.42% 62.35% 30.93% 6.94% 25.78% 52.70%
## 1003: 19.38% 62.35% 30.93% 6.86% 25.78% 52.70%
## 1004: 19.42% 62.35% 30.72% 7.02% 25.78% 52.70%
## 1005: 19.42% 62.35% 30.72% 7.02% 25.78% 52.70%
## 1006: 19.42% 62.35% 30.72% 7.02% 25.78% 52.70%
## 1007: 19.42% 62.35% 30.72% 7.02% 25.78% 52.70%
## 1008: 19.38% 62.35% 30.51% 7.02% 25.78% 52.70%
## 1009: 19.38% 62.35% 30.72% 6.94% 25.78% 52.70%
## 1010: 19.30% 62.35% 30.51% 6.94% 25.63% 52.70%
## 1011: 19.34% 62.35% 30.51% 6.94% 25.78% 52.70%
## 1012: 19.30% 62.35% 30.72% 6.78% 25.78% 52.70%
## 1013: 19.38% 62.35% 30.72% 6.94% 25.78% 52.70%
## 1014: 19.34% 62.35% 30.72% 6.86% 25.78% 52.70%
## 1015: 19.30% 62.35% 30.51% 6.94% 25.63% 52.70%
## 1016: 19.30% 62.35% 30.72% 6.94% 25.48% 52.70%
## 1017: 19.26% 62.35% 30.72% 6.86% 25.48% 52.70%
## 1018: 19.26% 62.35% 30.72% 6.94% 25.34% 52.70%
## 1019: 19.30% 62.35% 30.93% 6.78% 25.63% 52.70%
## 1020: 19.30% 62.35% 30.72% 6.86% 25.63% 52.70%
## 1021: 19.38% 62.35% 30.72% 7.02% 25.63% 52.70%
## 1022: 19.34% 62.35% 30.72% 6.94% 25.63% 52.70%
## 1023: 19.22% 62.35% 30.51% 6.86% 25.48% 52.70%
## 1024: 19.14% 62.35% 30.30% 6.86% 25.34% 52.70%
## 1025: 19.22% 62.35% 30.30% 6.94% 25.48% 52.70%
## 1026: 19.30% 62.35% 30.72% 6.86% 25.63% 52.70%
## 1027: 19.30% 62.35% 30.51% 6.94% 25.63% 52.70%
## 1028: 19.26% 62.35% 30.30% 7.02% 25.48% 52.70%
## 1029: 19.34% 62.35% 30.72% 7.02% 25.48% 52.70%
## 1030: 19.22% 62.35% 30.72% 6.86% 25.34% 52.70%
## 1031: 19.38% 62.35% 30.93% 7.10% 25.34% 52.70%
## 1032: 19.46% 62.35% 31.14% 7.02% 25.63% 52.70%
## 1033: 19.30% 62.35% 30.93% 6.94% 25.34% 52.70%
## 1034: 19.26% 62.35% 30.72% 6.94% 25.34% 52.70%
## 1035: 19.30% 62.35% 30.72% 6.94% 25.48% 52.70%
## 1036: 19.26% 62.35% 30.72% 6.86% 25.48% 52.70%
## 1037: 19.30% 62.35% 30.72% 6.94% 25.48% 52.70%
## 1038: 19.30% 62.35% 30.72% 6.94% 25.48% 52.70%
## 1039: 19.30% 62.35% 30.72% 6.86% 25.63% 52.70%
## 1040: 19.34% 62.35% 30.93% 6.94% 25.48% 52.70%
## 1041: 19.30% 62.35% 30.93% 6.86% 25.48% 52.70%
## 1042: 19.42% 62.35% 31.14% 7.02% 25.48% 52.70%
## 1043: 19.38% 62.35% 30.72% 7.02% 25.63% 52.70%
## 1044: 19.46% 62.35% 31.14% 7.10% 25.48% 52.70%
## 1045: 19.34% 62.35% 30.93% 7.02% 25.34% 52.70%
## 1046: 19.46% 62.35% 30.93% 7.10% 25.63% 52.70%
## 1047: 19.42% 62.35% 30.72% 7.10% 25.63% 52.70%
## 1048: 19.46% 62.35% 30.72% 7.10% 25.78% 52.70%
## 1049: 19.42% 62.35% 30.72% 7.10% 25.63% 52.70%
## 1050: 19.38% 62.35% 30.51% 7.02% 25.78% 52.70%
## 1051: 19.34% 62.35% 30.51% 7.02% 25.63% 52.70%
## 1052: 19.42% 62.35% 30.72% 7.02% 25.78% 52.70%
## 1053: 19.38% 62.35% 30.51% 7.10% 25.63% 52.70%
## 1054: 19.34% 62.35% 30.72% 7.02% 25.48% 52.70%
## 1055: 19.46% 62.35% 30.72% 7.10% 25.78% 52.70%
## 1056: 19.38% 62.35% 30.72% 6.94% 25.78% 52.70%
## 1057: 19.30% 62.35% 30.72% 6.94% 25.48% 52.70%
## 1058: 19.38% 62.35% 30.72% 6.94% 25.78% 52.70%
## 1059: 19.38% 62.35% 30.72% 6.94% 25.78% 52.70%
## 1060: 19.46% 62.35% 30.93% 6.94% 25.93% 52.70%
## 1061: 19.46% 62.35% 30.93% 6.94% 25.93% 52.70%
## 1062: 19.38% 62.35% 30.72% 6.94% 25.78% 52.70%
## 1063: 19.38% 62.35% 30.51% 6.94% 25.93% 52.70%
## 1064: 19.34% 62.35% 30.30% 6.94% 25.93% 52.70%
## 1065: 19.42% 62.35% 30.72% 6.94% 25.93% 52.70%
## 1066: 19.34% 62.35% 30.30% 6.94% 25.93% 52.70%
## 1067: 19.30% 62.35% 30.30% 6.86% 25.93% 52.70%
## 1068: 19.34% 62.35% 30.30% 6.94% 25.93% 52.70%
## 1069: 19.30% 62.35% 30.30% 6.86% 25.93% 52.70%
## 1070: 19.46% 62.35% 30.93% 6.94% 25.93% 52.70%
## 1071: 19.38% 62.35% 30.72% 6.86% 25.93% 52.70%
## 1072: 19.49% 62.35% 30.93% 6.94% 26.08% 52.70%
## 1073: 19.46% 62.35% 30.72% 6.94% 26.08% 52.70%
## 1074: 19.34% 62.35% 30.30% 6.94% 25.93% 52.70%
## 1075: 19.30% 62.35% 30.30% 6.86% 25.93% 52.70%
## 1076: 19.38% 63.53% 30.51% 6.86% 25.93% 52.70%
## 1077: 19.34% 63.53% 30.30% 6.86% 25.93% 52.70%
## 1078: 19.38% 63.53% 30.30% 6.86% 26.08% 52.70%
## 1079: 19.38% 62.35% 30.30% 6.94% 26.08% 52.70%
## 1080: 19.26% 62.35% 30.08% 6.86% 25.93% 52.70%
## 1081: 19.30% 62.35% 30.08% 6.94% 25.93% 52.70%
## 1082: 19.34% 62.35% 30.30% 6.94% 25.93% 52.70%
## 1083: 19.34% 62.35% 30.30% 6.94% 25.93% 52.70%
## 1084: 19.30% 62.35% 30.08% 6.94% 25.93% 52.70%
## 1085: 19.42% 62.35% 30.51% 6.94% 26.08% 52.70%
## 1086: 19.22% 61.18% 30.08% 6.94% 25.78% 52.70%
## 1087: 19.22% 61.18% 30.30% 6.86% 25.78% 52.70%
## 1088: 19.18% 61.18% 30.08% 6.86% 25.78% 52.70%
## 1089: 19.14% 61.18% 30.08% 6.86% 25.63% 52.70%
## 1090: 19.18% 61.18% 30.08% 6.78% 25.93% 52.70%
## 1091: 19.34% 61.18% 30.08% 7.02% 26.08% 52.70%
## 1092: 19.18% 61.18% 29.87% 6.94% 25.78% 52.70%
## 1093: 19.22% 61.18% 30.30% 6.78% 25.93% 52.70%
## 1094: 19.22% 61.18% 30.08% 6.86% 25.93% 52.70%
## 1095: 19.22% 61.18% 30.08% 6.86% 25.93% 52.70%
## 1096: 19.38% 61.18% 30.72% 6.86% 26.08% 52.70%
## 1097: 19.26% 61.18% 30.30% 6.86% 25.93% 52.70%
## 1098: 19.30% 62.35% 29.87% 6.94% 26.08% 52.70%
## 1099: 19.38% 61.18% 30.72% 6.94% 25.93% 52.70%
## 1100: 19.34% 62.35% 30.30% 6.86% 26.08% 52.70%
## 1101: 19.26% 61.18% 30.30% 6.86% 25.93% 52.70%
## 1102: 19.30% 61.18% 30.30% 6.94% 25.93% 52.70%
## 1103: 19.26% 61.18% 30.30% 6.86% 25.93% 52.70%
## 1104: 19.30% 61.18% 30.30% 6.94% 25.93% 52.70%
## 1105: 19.34% 62.35% 30.30% 6.94% 25.93% 52.70%
## 1106: 19.49% 62.35% 30.93% 6.94% 26.08% 52.70%
## 1107: 19.42% 63.53% 30.51% 6.94% 25.93% 52.70%
## 1108: 19.42% 63.53% 30.51% 6.94% 25.93% 52.70%
## 1109: 19.34% 62.35% 30.30% 6.94% 25.93% 52.70%
## 1110: 19.34% 62.35% 30.30% 6.94% 25.93% 52.70%
## 1111: 19.38% 62.35% 30.51% 6.94% 25.93% 52.70%
## 1112: 19.30% 62.35% 30.30% 6.94% 25.78% 52.70%
## 1113: 19.42% 63.53% 30.51% 6.94% 25.93% 52.70%
## 1114: 19.34% 62.35% 30.51% 6.94% 25.78% 52.70%
## 1115: 19.38% 62.35% 30.51% 7.02% 25.78% 52.70%
## 1116: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1117: 19.46% 64.71% 30.51% 7.02% 25.78% 52.70%
## 1118: 19.38% 62.35% 30.51% 7.02% 25.78% 52.70%
## 1119: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1120: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1121: 19.46% 64.71% 30.30% 7.10% 25.78% 52.70%
## 1122: 19.42% 64.71% 30.30% 7.02% 25.78% 52.70%
## 1123: 19.42% 64.71% 30.30% 7.02% 25.78% 52.70%
## 1124: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1125: 19.46% 64.71% 30.30% 7.10% 25.78% 52.70%
## 1126: 19.38% 64.71% 30.08% 7.02% 25.78% 52.70%
## 1127: 19.42% 64.71% 30.30% 7.02% 25.78% 52.70%
## 1128: 19.34% 64.71% 30.08% 7.02% 25.63% 52.70%
## 1129: 19.38% 64.71% 30.30% 7.02% 25.63% 52.70%
## 1130: 19.34% 64.71% 30.30% 7.02% 25.48% 52.70%
## 1131: 19.34% 64.71% 30.30% 7.02% 25.48% 52.70%
## 1132: 19.30% 64.71% 30.08% 7.02% 25.48% 52.70%
## 1133: 19.26% 64.71% 30.30% 6.86% 25.48% 52.70%
## 1134: 19.34% 64.71% 30.51% 6.86% 25.63% 52.70%
## 1135: 19.30% 63.53% 30.30% 6.94% 25.63% 52.70%
## 1136: 19.26% 63.53% 30.30% 6.86% 25.63% 52.70%
## 1137: 19.18% 63.53% 30.30% 6.86% 25.34% 52.70%
## 1138: 19.22% 63.53% 30.08% 6.94% 25.48% 52.70%
## 1139: 19.30% 64.71% 30.30% 6.94% 25.48% 52.70%
## 1140: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1141: 19.38% 63.53% 30.51% 6.94% 25.78% 52.70%
## 1142: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1143: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1144: 19.34% 63.53% 30.72% 6.86% 25.63% 52.70%
## 1145: 19.38% 63.53% 30.51% 6.94% 25.78% 52.70%
## 1146: 19.34% 63.53% 30.51% 6.86% 25.78% 52.70%
## 1147: 19.30% 63.53% 30.51% 6.86% 25.63% 52.70%
## 1148: 19.34% 63.53% 30.51% 6.94% 25.63% 52.70%
## 1149: 19.26% 63.53% 30.30% 6.86% 25.63% 52.70%
## 1150: 19.34% 64.71% 30.30% 6.86% 25.78% 52.70%
## 1151: 19.34% 63.53% 30.51% 6.86% 25.78% 52.70%
## 1152: 19.42% 63.53% 30.93% 6.94% 25.63% 52.70%
## 1153: 19.30% 63.53% 30.72% 6.86% 25.48% 52.70%
## 1154: 19.34% 63.53% 30.72% 6.94% 25.48% 52.70%
## 1155: 19.34% 63.53% 30.72% 6.94% 25.48% 52.70%
## 1156: 19.46% 63.53% 30.93% 6.94% 25.78% 52.70%
## 1157: 19.42% 63.53% 30.93% 6.86% 25.78% 52.70%
## 1158: 19.42% 63.53% 30.93% 6.86% 25.78% 52.70%
## 1159: 19.46% 63.53% 30.93% 6.94% 25.78% 52.70%
## 1160: 19.46% 63.53% 30.93% 6.94% 25.78% 52.70%
## 1161: 19.46% 63.53% 30.93% 6.94% 25.78% 52.70%
## 1162: 19.38% 63.53% 30.51% 6.94% 25.78% 52.70%
## 1163: 19.38% 63.53% 30.51% 6.94% 25.78% 52.70%
## 1164: 19.42% 63.53% 30.72% 6.94% 25.78% 52.70%
## 1165: 19.42% 63.53% 30.72% 6.94% 25.78% 52.70%
## 1166: 19.42% 63.53% 30.72% 6.94% 25.78% 52.70%
## 1167: 19.42% 63.53% 30.72% 6.94% 25.78% 52.70%
## 1168: 19.42% 63.53% 30.72% 6.94% 25.78% 52.70%
## 1169: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1170: 19.34% 63.53% 30.51% 6.94% 25.63% 52.70%
## 1171: 19.38% 63.53% 30.51% 7.02% 25.63% 52.70%
## 1172: 19.34% 63.53% 30.30% 7.02% 25.63% 52.70%
## 1173: 19.22% 63.53% 30.08% 6.94% 25.48% 52.70%
## 1174: 19.34% 63.53% 30.30% 7.02% 25.63% 52.70%
## 1175: 19.30% 64.71% 30.08% 6.94% 25.63% 52.70%
## 1176: 19.38% 64.71% 30.30% 7.02% 25.63% 52.70%
## 1177: 19.30% 64.71% 30.08% 6.94% 25.63% 52.70%
## 1178: 19.30% 64.71% 30.08% 6.94% 25.63% 52.70%
## 1179: 19.38% 64.71% 30.51% 6.94% 25.63% 52.70%
## 1180: 19.34% 64.71% 30.51% 6.86% 25.63% 52.70%
## 1181: 19.38% 64.71% 30.51% 7.02% 25.48% 52.70%
## 1182: 19.38% 64.71% 30.51% 7.02% 25.48% 52.70%
## 1183: 19.38% 64.71% 30.51% 7.02% 25.48% 52.70%
## 1184: 19.34% 64.71% 30.51% 6.94% 25.48% 52.70%
## 1185: 19.26% 64.71% 30.51% 6.78% 25.48% 52.70%
## 1186: 19.38% 64.71% 30.30% 7.02% 25.63% 52.70%
## 1187: 19.34% 64.71% 30.51% 6.86% 25.63% 52.70%
## 1188: 19.38% 64.71% 30.51% 6.94% 25.63% 52.70%
## 1189: 19.38% 64.71% 30.72% 6.86% 25.63% 52.70%
## 1190: 19.30% 64.71% 30.30% 6.86% 25.63% 52.70%
## 1191: 19.34% 64.71% 30.51% 6.86% 25.63% 52.70%
## 1192: 19.30% 64.71% 30.30% 6.86% 25.63% 52.70%
## 1193: 19.42% 63.53% 30.93% 7.02% 25.48% 52.70%
## 1194: 19.26% 63.53% 30.08% 6.94% 25.63% 52.70%
## 1195: 19.38% 63.53% 30.51% 7.02% 25.63% 52.70%
## 1196: 19.53% 64.71% 31.14% 7.02% 25.63% 52.70%
## 1197: 19.46% 63.53% 30.93% 6.94% 25.78% 52.70%
## 1198: 19.46% 63.53% 30.72% 7.02% 25.78% 52.70%
## 1199: 19.46% 64.71% 30.72% 7.02% 25.63% 52.70%
## 1200: 19.38% 63.53% 30.72% 6.94% 25.63% 52.70%
## 1201: 19.42% 63.53% 30.93% 6.94% 25.63% 52.70%
## 1202: 19.42% 63.53% 30.72% 7.02% 25.63% 52.70%
## 1203: 19.30% 63.53% 30.72% 6.78% 25.63% 52.70%
## 1204: 19.34% 63.53% 30.72% 6.86% 25.63% 52.70%
## 1205: 19.22% 62.35% 30.51% 6.78% 25.63% 52.70%
## 1206: 19.22% 63.53% 30.08% 6.78% 25.78% 52.70%
## 1207: 19.30% 63.53% 30.51% 6.78% 25.78% 52.70%
## 1208: 19.30% 62.35% 30.51% 6.78% 25.93% 52.70%
## 1209: 19.30% 63.53% 30.51% 6.86% 25.63% 52.70%
## 1210: 19.38% 62.35% 30.72% 6.94% 25.78% 52.70%
## 1211: 19.38% 62.35% 30.51% 6.94% 25.93% 52.70%
## 1212: 19.42% 63.53% 30.72% 6.94% 25.78% 52.70%
## 1213: 19.34% 62.35% 30.72% 6.86% 25.78% 52.70%
## 1214: 19.38% 63.53% 30.51% 6.86% 25.93% 52.70%
## 1215: 19.42% 62.35% 30.72% 6.94% 25.93% 52.70%
## 1216: 19.26% 62.35% 30.08% 6.86% 25.93% 52.70%
## 1217: 19.30% 62.35% 30.08% 6.94% 25.93% 52.70%
## 1218: 19.34% 62.35% 30.08% 6.94% 26.08% 52.70%
## 1219: 19.42% 63.53% 30.08% 7.02% 26.08% 52.70%
## 1220: 19.38% 63.53% 30.08% 6.94% 26.08% 52.70%
## 1221: 19.34% 63.53% 30.08% 6.94% 25.93% 52.70%
## 1222: 19.38% 63.53% 30.08% 6.94% 26.08% 52.70%
## 1223: 19.38% 63.53% 30.08% 6.86% 26.23% 52.70%
## 1224: 19.34% 63.53% 29.87% 6.94% 26.08% 52.70%
## 1225: 19.49% 63.53% 30.72% 6.94% 26.08% 52.70%
## 1226: 19.42% 63.53% 30.51% 6.94% 25.93% 52.70%
## 1227: 19.30% 63.53% 30.51% 6.94% 25.48% 52.70%
## 1228: 19.38% 63.53% 30.51% 6.94% 25.78% 52.70%
## 1229: 19.30% 63.53% 30.51% 6.94% 25.48% 52.70%
## 1230: 19.38% 63.53% 30.51% 7.02% 25.63% 52.70%
## 1231: 19.34% 63.53% 30.51% 6.94% 25.63% 52.70%
## 1232: 19.46% 63.53% 30.51% 7.02% 25.93% 52.70%
## 1233: 19.53% 63.53% 30.72% 7.02% 26.08% 52.70%
## 1234: 19.34% 63.53% 30.30% 7.02% 25.63% 52.70%
## 1235: 19.26% 63.53% 30.08% 7.02% 25.48% 52.70%
## 1236: 19.38% 63.53% 30.08% 7.02% 25.93% 52.70%
## 1237: 19.38% 63.53% 30.08% 7.02% 25.93% 52.70%
## 1238: 19.34% 63.53% 30.08% 7.02% 25.78% 52.70%
## 1239: 19.34% 63.53% 30.08% 7.02% 25.78% 52.70%
## 1240: 19.34% 63.53% 30.08% 7.02% 25.78% 52.70%
## 1241: 19.34% 63.53% 29.87% 7.02% 25.93% 52.70%
## 1242: 19.30% 63.53% 29.87% 7.02% 25.78% 52.70%
## 1243: 19.30% 63.53% 29.87% 7.02% 25.78% 52.70%
## 1244: 19.34% 63.53% 30.08% 7.02% 25.78% 52.70%
## 1245: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1246: 19.42% 63.53% 30.30% 7.02% 25.93% 52.70%
## 1247: 19.38% 63.53% 30.08% 7.02% 25.93% 52.70%
## 1248: 19.38% 63.53% 30.30% 6.94% 25.93% 52.70%
## 1249: 19.38% 63.53% 30.30% 6.94% 25.93% 52.70%
## 1250: 19.38% 63.53% 30.30% 6.94% 25.93% 52.70%
## 1251: 19.38% 63.53% 30.30% 6.94% 25.93% 52.70%
## 1252: 19.46% 63.53% 30.51% 7.02% 25.93% 52.70%
## 1253: 19.42% 63.53% 30.51% 6.94% 25.93% 52.70%
## 1254: 19.38% 63.53% 30.30% 6.94% 25.93% 52.70%
## 1255: 19.42% 63.53% 30.30% 7.02% 25.93% 52.70%
## 1256: 19.42% 63.53% 30.51% 6.94% 25.93% 52.70%
## 1257: 19.38% 63.53% 30.51% 6.94% 25.78% 52.70%
## 1258: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1259: 19.34% 63.53% 30.51% 6.86% 25.78% 52.70%
## 1260: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1261: 19.34% 63.53% 30.51% 6.86% 25.78% 52.70%
## 1262: 19.38% 63.53% 30.51% 6.94% 25.78% 52.70%
## 1263: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1264: 19.30% 63.53% 30.30% 6.86% 25.78% 52.70%
## 1265: 19.30% 63.53% 30.30% 6.86% 25.78% 52.70%
## 1266: 19.30% 63.53% 30.30% 6.86% 25.78% 52.70%
## 1267: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1268: 19.30% 63.53% 30.30% 6.86% 25.78% 52.70%
## 1269: 19.30% 63.53% 30.30% 6.86% 25.78% 52.70%
## 1270: 19.30% 63.53% 30.30% 6.86% 25.78% 52.70%
## 1271: 19.30% 63.53% 30.30% 6.86% 25.78% 52.70%
## 1272: 19.30% 63.53% 30.30% 6.86% 25.78% 52.70%
## 1273: 19.30% 63.53% 30.30% 6.86% 25.78% 52.70%
## 1274: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1275: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1276: 19.30% 63.53% 30.30% 6.86% 25.78% 52.70%
## 1277: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1278: 19.26% 63.53% 30.30% 6.78% 25.78% 52.70%
## 1279: 19.26% 63.53% 30.30% 6.86% 25.63% 52.70%
## 1280: 19.22% 63.53% 30.30% 6.78% 25.63% 52.70%
## 1281: 19.30% 63.53% 30.30% 6.86% 25.78% 52.70%
## 1282: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1283: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1284: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1285: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1286: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1287: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1288: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1289: 19.42% 63.53% 30.51% 7.02% 25.78% 52.70%
## 1290: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1291: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1292: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1293: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1294: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1295: 19.38% 63.53% 30.30% 7.02% 25.78% 52.70%
## 1296: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1297: 19.30% 63.53% 30.08% 7.02% 25.63% 52.70%
## 1298: 19.26% 63.53% 30.08% 6.94% 25.63% 52.70%
## 1299: 19.34% 63.53% 30.30% 6.94% 25.78% 52.70%
## 1300: 19.30% 63.53% 30.08% 6.94% 25.78% 52.70%
## 1301: 19.34% 63.53% 30.08% 7.02% 25.78% 52.70%
## 1302: 19.34% 63.53% 30.08% 7.02% 25.63% 54.05%
## 1303: 19.38% 63.53% 30.08% 7.02% 25.78% 54.05%
## 1304: 19.38% 63.53% 30.08% 7.02% 25.78% 54.05%
## 1305: 19.38% 63.53% 30.08% 7.02% 25.78% 54.05%
## 1306: 19.42% 63.53% 30.30% 7.02% 25.78% 54.05%
## 1307: 19.42% 63.53% 30.30% 7.10% 25.78% 52.70%
## 1308: 19.42% 63.53% 30.30% 7.10% 25.78% 52.70%
## 1309: 19.38% 63.53% 30.08% 7.10% 25.63% 54.05%
## 1310: 19.38% 63.53% 29.87% 7.10% 25.78% 54.05%
## 1311: 19.46% 63.53% 30.51% 7.02% 25.78% 54.05%
## 1312: 19.46% 63.53% 30.30% 7.10% 25.78% 54.05%
## 1313: 19.42% 63.53% 30.08% 7.10% 25.78% 54.05%
## 1314: 19.46% 63.53% 30.30% 7.10% 25.78% 54.05%
## 1315: 19.49% 63.53% 30.51% 7.10% 25.78% 54.05%
## 1316: 19.42% 63.53% 30.30% 7.02% 25.78% 54.05%
## 1317: 19.42% 63.53% 30.30% 7.02% 25.78% 54.05%
## 1318: 19.38% 63.53% 30.30% 7.02% 25.63% 54.05%
## 1319: 19.38% 63.53% 30.51% 6.94% 25.63% 54.05%
## 1320: 19.42% 63.53% 30.51% 7.02% 25.63% 54.05%
## 1321: 19.46% 63.53% 30.51% 7.10% 25.63% 54.05%
## 1322: 19.49% 63.53% 30.51% 7.10% 25.78% 54.05%
## 1323: 19.42% 63.53% 30.30% 7.10% 25.63% 54.05%
## 1324: 19.42% 63.53% 30.30% 7.10% 25.63% 54.05%
## 1325: 19.38% 63.53% 30.30% 7.10% 25.48% 54.05%
## 1326: 19.42% 63.53% 30.30% 7.10% 25.63% 54.05%
## 1327: 19.42% 63.53% 30.51% 7.10% 25.48% 54.05%
## 1328: 19.46% 63.53% 30.51% 7.10% 25.63% 54.05%
## 1329: 19.46% 63.53% 30.51% 7.10% 25.63% 54.05%
## 1330: 19.42% 63.53% 30.51% 7.02% 25.63% 54.05%
## 1331: 19.46% 63.53% 30.51% 7.10% 25.63% 54.05%
## 1332: 19.46% 63.53% 30.51% 7.10% 25.63% 54.05%
## 1333: 19.49% 63.53% 30.51% 7.18% 25.63% 54.05%
## 1334: 19.42% 63.53% 30.51% 7.02% 25.63% 54.05%
## 1335: 19.53% 63.53% 30.72% 7.18% 25.63% 54.05%
## 1336: 19.53% 63.53% 30.72% 7.18% 25.63% 54.05%
## 1337: 19.49% 63.53% 30.51% 7.18% 25.63% 54.05%
## 1338: 19.46% 63.53% 30.51% 7.10% 25.63% 54.05%
## 1339: 19.42% 63.53% 30.51% 7.10% 25.48% 54.05%
## 1340: 19.42% 63.53% 30.51% 7.10% 25.48% 54.05%
## 1341: 19.34% 63.53% 30.51% 7.02% 25.34% 54.05%
## 1342: 19.42% 63.53% 30.51% 7.10% 25.48% 54.05%
## 1343: 19.42% 63.53% 30.51% 7.10% 25.48% 54.05%
## 1344: 19.46% 63.53% 30.72% 7.10% 25.48% 54.05%
## 1345: 19.38% 63.53% 30.51% 7.10% 25.34% 54.05%
## 1346: 19.49% 63.53% 30.72% 7.18% 25.48% 54.05%
## 1347: 19.46% 63.53% 30.51% 7.18% 25.48% 54.05%
## 1348: 19.42% 63.53% 30.51% 7.18% 25.34% 54.05%
## 1349: 19.42% 63.53% 30.72% 7.10% 25.34% 54.05%
## 1350: 19.38% 63.53% 30.51% 7.10% 25.34% 54.05%
## 1351: 19.38% 63.53% 30.51% 7.10% 25.34% 54.05%
## 1352: 19.38% 63.53% 30.51% 7.10% 25.34% 54.05%
## 1353: 19.42% 63.53% 30.51% 7.10% 25.48% 54.05%
## 1354: 19.46% 63.53% 30.51% 7.18% 25.48% 54.05%
## 1355: 19.53% 63.53% 30.72% 7.26% 25.48% 54.05%
## 1356: 19.42% 63.53% 30.30% 7.18% 25.48% 54.05%
## 1357: 19.42% 63.53% 30.51% 7.10% 25.48% 54.05%
## 1358: 19.46% 63.53% 30.72% 7.10% 25.48% 54.05%
## 1359: 19.46% 63.53% 30.51% 7.18% 25.48% 54.05%
## 1360: 19.46% 63.53% 30.51% 7.18% 25.48% 54.05%
## 1361: 19.46% 63.53% 30.51% 7.18% 25.48% 54.05%
## 1362: 19.46% 63.53% 30.51% 7.18% 25.48% 54.05%
## 1363: 19.49% 63.53% 30.72% 7.18% 25.48% 54.05%
## 1364: 19.42% 63.53% 30.30% 7.18% 25.48% 54.05%
## 1365: 19.42% 63.53% 30.30% 7.18% 25.48% 54.05%
## 1366: 19.46% 63.53% 30.51% 7.18% 25.48% 54.05%
## 1367: 19.34% 63.53% 30.30% 7.18% 25.34% 52.70%
## 1368: 19.38% 63.53% 30.30% 7.18% 25.48% 52.70%
## 1369: 19.46% 63.53% 30.51% 7.18% 25.48% 54.05%
## 1370: 19.42% 63.53% 30.08% 7.26% 25.48% 54.05%
## 1371: 19.46% 63.53% 30.30% 7.26% 25.48% 54.05%
## 1372: 19.42% 63.53% 30.30% 7.18% 25.48% 54.05%
## 1373: 19.42% 63.53% 30.30% 7.18% 25.48% 54.05%
## 1374: 19.38% 63.53% 30.30% 7.18% 25.34% 54.05%
## 1375: 19.46% 63.53% 30.51% 7.18% 25.48% 54.05%
## 1376: 19.49% 63.53% 30.51% 7.26% 25.48% 54.05%
## 1377: 19.42% 63.53% 30.30% 7.18% 25.48% 54.05%
## 1378: 19.46% 63.53% 30.51% 7.18% 25.48% 54.05%
## 1379: 19.34% 63.53% 29.87% 7.18% 25.48% 54.05%
## 1380: 19.34% 63.53% 29.87% 7.18% 25.48% 54.05%
## 1381: 19.34% 63.53% 29.87% 7.18% 25.48% 54.05%
## 1382: 19.38% 63.53% 30.08% 7.18% 25.48% 54.05%
## 1383: 19.34% 63.53% 29.87% 7.18% 25.48% 54.05%
## 1384: 19.38% 63.53% 30.08% 7.18% 25.48% 54.05%
## 1385: 19.42% 63.53% 30.08% 7.26% 25.48% 54.05%
## 1386: 19.46% 63.53% 30.30% 7.26% 25.48% 54.05%
## 1387: 19.34% 63.53% 30.30% 7.18% 25.19% 54.05%
## 1388: 19.38% 63.53% 30.30% 7.18% 25.34% 54.05%
## 1389: 19.38% 63.53% 30.30% 7.18% 25.34% 54.05%
## 1390: 19.38% 63.53% 30.30% 7.18% 25.34% 54.05%
## 1391: 19.38% 63.53% 30.30% 7.18% 25.34% 54.05%
## 1392: 19.42% 63.53% 30.51% 7.18% 25.34% 54.05%
## 1393: 19.46% 63.53% 30.51% 7.18% 25.48% 54.05%
## 1394: 19.30% 63.53% 30.08% 7.18% 25.19% 54.05%
## 1395: 19.30% 63.53% 30.08% 7.18% 25.19% 54.05%
## 1396: 19.26% 63.53% 30.08% 7.18% 25.04% 54.05%
## 1397: 19.26% 63.53% 30.08% 7.10% 25.19% 54.05%
## 1398: 19.34% 63.53% 30.30% 7.18% 25.19% 54.05%
## 1399: 19.30% 63.53% 30.30% 7.02% 25.34% 54.05%
## 1400: 19.30% 63.53% 30.08% 7.18% 25.19% 54.05%
## 1401: 19.22% 63.53% 30.08% 7.10% 25.04% 54.05%
## 1402: 19.30% 63.53% 30.08% 7.18% 25.19% 54.05%
## 1403: 19.34% 63.53% 30.08% 7.18% 25.34% 54.05%
## 1404: 19.30% 63.53% 30.30% 7.10% 25.19% 54.05%
## 1405: 19.38% 63.53% 30.30% 7.18% 25.34% 54.05%
## 1406: 19.42% 63.53% 30.30% 7.26% 25.34% 54.05%
## 1407: 19.26% 63.53% 30.08% 7.10% 25.19% 54.05%
## 1408: 19.34% 63.53% 30.08% 7.18% 25.34% 54.05%
## 1409: 19.34% 63.53% 30.08% 7.18% 25.34% 54.05%
## 1410: 19.26% 63.53% 30.08% 7.18% 25.04% 54.05%
## 1411: 19.30% 63.53% 30.08% 7.10% 25.34% 54.05%
## 1412: 19.26% 63.53% 30.08% 7.18% 25.04% 54.05%
## 1413: 19.34% 63.53% 30.08% 7.10% 25.48% 54.05%
## 1414: 19.42% 63.53% 30.08% 7.18% 25.63% 54.05%
## 1415: 19.38% 63.53% 30.30% 7.26% 25.19% 54.05%
## 1416: 19.38% 63.53% 30.51% 7.18% 25.19% 54.05%
## 1417: 19.34% 63.53% 30.51% 7.18% 25.04% 54.05%
## 1418: 19.38% 63.53% 30.72% 7.10% 25.19% 54.05%
## 1419: 19.49% 63.53% 30.72% 7.26% 25.34% 54.05%
## 1420: 19.38% 63.53% 30.30% 7.26% 25.19% 54.05%
## 1421: 19.34% 63.53% 30.30% 7.18% 25.19% 54.05%
## 1422: 19.18% 63.53% 30.08% 7.18% 24.74% 54.05%
## 1423: 19.26% 63.53% 30.08% 7.26% 24.89% 54.05%
## 1424: 19.30% 63.53% 30.08% 7.26% 25.04% 54.05%
## 1425: 19.26% 63.53% 30.08% 7.18% 25.04% 54.05%
## 1426: 19.30% 63.53% 30.08% 7.18% 25.19% 54.05%
## 1427: 19.22% 63.53% 29.87% 7.18% 25.04% 54.05%
## 1428: 19.34% 63.53% 30.08% 7.26% 25.19% 54.05%
## 1429: 19.26% 63.53% 30.08% 7.18% 25.04% 54.05%
## 1430: 19.22% 63.53% 29.87% 7.26% 24.89% 54.05%
## 1431: 19.30% 63.53% 30.08% 7.26% 25.04% 54.05%
## 1432: 19.34% 63.53% 30.08% 7.26% 25.19% 54.05%
## 1433: 19.30% 63.53% 30.08% 7.26% 25.04% 54.05%
## 1434: 19.30% 63.53% 29.87% 7.26% 25.19% 54.05%
## 1435: 19.34% 63.53% 30.08% 7.33% 25.04% 54.05%
## 1436: 19.30% 63.53% 29.87% 7.33% 25.04% 54.05%
## 1437: 19.34% 63.53% 30.08% 7.26% 25.19% 54.05%
## 1438: 19.38% 63.53% 30.08% 7.33% 25.19% 54.05%
## 1439: 19.38% 63.53% 30.08% 7.33% 25.19% 54.05%
## 1440: 19.42% 63.53% 30.08% 7.33% 25.34% 54.05%
## 1441: 19.42% 63.53% 30.08% 7.33% 25.34% 54.05%
## 1442: 19.30% 63.53% 30.08% 7.26% 25.04% 54.05%
## 1443: 19.26% 63.53% 30.08% 7.18% 25.04% 54.05%
## 1444: 19.34% 63.53% 30.08% 7.26% 25.19% 54.05%
## 1445: 19.34% 63.53% 30.08% 7.26% 25.19% 54.05%
## 1446: 19.42% 63.53% 30.08% 7.33% 25.34% 54.05%
## 1447: 19.42% 63.53% 30.08% 7.33% 25.34% 54.05%
## 1448: 19.34% 63.53% 30.08% 7.26% 25.19% 54.05%
## 1449: 19.38% 63.53% 30.08% 7.33% 25.19% 54.05%
## 1450: 19.34% 63.53% 29.87% 7.33% 25.19% 54.05%
## 1451: 19.42% 63.53% 30.30% 7.33% 25.19% 54.05%
## 1452: 19.38% 63.53% 30.08% 7.33% 25.19% 54.05%
## 1453: 19.46% 63.53% 30.30% 7.33% 25.34% 54.05%
## 1454: 19.38% 63.53% 30.08% 7.33% 25.19% 54.05%
## 1455: 19.49% 63.53% 30.30% 7.41% 25.34% 54.05%
## 1456: 19.38% 63.53% 30.30% 7.33% 25.04% 54.05%
## 1457: 19.34% 63.53% 30.08% 7.33% 25.04% 54.05%
## 1458: 19.34% 63.53% 30.08% 7.33% 25.04% 54.05%
## 1459: 19.38% 63.53% 30.08% 7.33% 25.19% 54.05%
## 1460: 19.34% 63.53% 30.08% 7.33% 25.04% 54.05%
## 1461: 19.30% 63.53% 30.08% 7.33% 25.04% 52.70%
## 1462: 19.42% 63.53% 30.08% 7.49% 25.04% 54.05%
## 1463: 19.42% 63.53% 30.30% 7.49% 25.04% 52.70%
## 1464: 19.42% 63.53% 30.30% 7.49% 25.04% 52.70%
## 1465: 19.46% 63.53% 30.30% 7.49% 25.04% 54.05%
## 1466: 19.34% 63.53% 30.08% 7.41% 25.04% 52.70%
## 1467: 19.38% 63.53% 30.08% 7.41% 25.04% 54.05%
## 1468: 19.38% 63.53% 30.08% 7.49% 25.04% 52.70%
## 1469: 19.38% 63.53% 30.08% 7.49% 25.04% 52.70%
## 1470: 19.38% 63.53% 30.08% 7.49% 25.04% 52.70%
## 1471: 19.38% 63.53% 30.08% 7.33% 25.19% 54.05%
## 1472: 19.34% 63.53% 30.08% 7.33% 25.19% 52.70%
## 1473: 19.30% 63.53% 29.87% 7.33% 25.19% 52.70%
## 1474: 19.34% 63.53% 30.08% 7.33% 25.19% 52.70%
## 1475: 19.26% 63.53% 29.87% 7.26% 25.19% 52.70%
## 1476: 19.38% 63.53% 30.08% 7.41% 25.19% 52.70%
## 1477: 19.34% 63.53% 30.08% 7.33% 25.19% 52.70%
## 1478: 19.26% 63.53% 29.87% 7.26% 25.19% 52.70%
## 1479: 19.26% 63.53% 29.87% 7.26% 25.19% 52.70%
## 1480: 19.34% 63.53% 30.08% 7.33% 25.19% 52.70%
## 1481: 19.30% 63.53% 29.87% 7.33% 25.19% 52.70%
## 1482: 19.30% 63.53% 29.87% 7.33% 25.19% 52.70%
## 1483: 19.34% 63.53% 29.87% 7.41% 25.19% 52.70%
## 1484: 19.34% 63.53% 29.87% 7.33% 25.34% 52.70%
## 1485: 19.30% 63.53% 29.87% 7.33% 25.19% 52.70%
## 1486: 19.38% 63.53% 29.87% 7.41% 25.34% 52.70%
## 1487: 19.30% 63.53% 29.87% 7.33% 25.19% 52.70%
## 1488: 19.38% 63.53% 29.87% 7.41% 25.34% 52.70%
## 1489: 19.34% 63.53% 29.87% 7.33% 25.34% 52.70%
## 1490: 19.34% 63.53% 29.87% 7.33% 25.34% 52.70%
## 1491: 19.34% 63.53% 29.87% 7.33% 25.34% 52.70%
## 1492: 19.34% 63.53% 29.87% 7.33% 25.34% 52.70%
## 1493: 19.34% 63.53% 29.87% 7.33% 25.34% 52.70%
## 1494: 19.38% 63.53% 29.87% 7.41% 25.34% 52.70%
## 1495: 19.38% 63.53% 29.87% 7.41% 25.34% 52.70%
## 1496: 19.42% 63.53% 30.08% 7.41% 25.34% 52.70%
## 1497: 19.38% 63.53% 29.87% 7.41% 25.34% 52.70%
## 1498: 19.38% 63.53% 29.66% 7.41% 25.48% 52.70%
## 1499: 19.42% 63.53% 29.87% 7.41% 25.48% 52.70%
## 1500: 19.38% 63.53% 29.66% 7.41% 25.48% 52.70%test3 <- predict(model_rf3, newdata = test)
table(test3, test$Hospital.overall.rating)##
## test3 1 2 3 4 5
## 1 13 6 0 0 0
## 2 19 139 14 0 0
## 3 0 67 468 62 0
## 4 0 0 22 231 23
## 5 0 0 0 0 14summary(model_rf3)## Length Class Mode
## call 8 -none- call
## type 1 -none- character
## predicted 2570 factor numeric
## err.rate 9000 -none- numeric
## confusion 30 -none- numeric
## votes 12850 matrix numeric
## oob.times 2570 -none- numeric
## classes 5 -none- character
## importance 53 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 2570 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## terms 3 terms callconf_matrix3 <- confusionMatrix(test3, test$Hospital.overall.rating, positive = "Yes")
conf_matrix3## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5
## 1 13 6 0 0 0
## 2 19 139 14 0 0
## 3 0 67 468 62 0
## 4 0 0 22 231 23
## 5 0 0 0 0 14
##
## Overall Statistics
##
## Accuracy : 0.8024
## 95% CI : (0.7774, 0.8258)
## No Information Rate : 0.4675
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6909
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
## Sensitivity 0.40625 0.6557 0.9286 0.7884 0.37838
## Specificity 0.99426 0.9619 0.7753 0.9427 1.00000
## Pos Pred Value 0.68421 0.8081 0.7839 0.8370 1.00000
## Neg Pred Value 0.98206 0.9194 0.9252 0.9227 0.97838
## Prevalence 0.02968 0.1967 0.4675 0.2718 0.03432
## Detection Rate 0.01206 0.1289 0.4341 0.2143 0.01299
## Detection Prevalence 0.01763 0.1596 0.5538 0.2560 0.01299
## Balanced Accuracy 0.70026 0.8088 0.8519 0.8655 0.68919# Confusion Matrix and Statistics
#
# Reference
# Prediction 1 2 3 4 5
# 1 13 6 0 0 0
# 2 19 139 14 0 0
# 3 0 67 468 62 0
# 4 0 0 22 231 23
# 5 0 0 0 0 14
#
# Overall Statistics
#
# Accuracy : 0.8024
# 95% CI : (0.7774, 0.8258)
# No Information Rate : 0.4675
# P-Value [Acc > NIR] : < 2.2e-16
#
# Kappa : 0.6909
# Mcnemar's Test P-Value : NA
#
# Statistics by Class:
#
# Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
# Sensitivity 0.40625 0.6557 0.9286 0.7884 0.37838
# Specificity 0.99426 0.9619 0.7753 0.9427 1.00000
# Pos Pred Value 0.68421 0.8081 0.7839 0.8370 1.00000
# Neg Pred Value 0.98206 0.9194 0.9252 0.9227 0.97838
# Prevalence 0.02968 0.1967 0.4675 0.2718 0.03432
# Detection Rate 0.01206 0.1289 0.4341 0.2143 0.01299
# Detection Prevalence 0.01763 0.1596 0.5538 0.2560 0.01299
# Balanced Accuracy 0.70026 0.8088 0.8519 0.8655 0.68919
# Accuracy: 80.24%
# We observe 80.24% accuracy with this model, all the classes have good specificity and sensitivity.
print(model_rf3)##
## Call:
## randomForest(formula = Hospital.overall.rating ~ ., data = train, promiximity = FALSE, ntree = 1500, mtry = 20, do.trace = TRUE, na.action = na.omit)
## Type of random forest: classification
## Number of trees: 1500
## No. of variables tried at each split: 20
##
## OOB estimate of error rate: 19.38%
## Confusion matrix:
## 1 2 3 4 5 class.error
## 1 31 54 0 0 0 0.63529412
## 2 5 332 135 0 0 0.29661017
## 3 0 42 1174 52 0 0.07413249
## 4 0 0 169 500 2 0.25484352
## 5 0 0 0 39 35 0.52702703# Call:
# randomForest(formula = Hospital.overall.rating ~ ., data = train, promiximity = FALSE, ntree = 1500, mtry = 20, do.trace = TRUE, na.action = na.omit)
# Type of random forest: classification
# Number of trees: 1500
# No. of variables tried at each split: 20
#
# OOB estimate of error rate: 19.38%
# Confusion matrix:
# 1 2 3 4 5 class.error
# 1 31 54 0 0 0 0.63529412
# 2 5 332 135 0 0 0.29661017
# 3 0 42 1174 52 0 0.07413249
# 4 0 0 169 500 2 0.25484352
# 5 0 0 0 39 35 0.52702703
# This plot provides us class errors.
plot(model_rf3)
plot(margin(model_rf3, test$Hospital.overall.rating))
# We see here most of the values >0 mean that the majority was right, hence this has predicted correct
# This plot provides us all the variables as per their importance in the model to predict the ratings.
varImpPlot(model_rf3)
# This provides the top 10 measures which are highly important as per the variance Importance measures.
varImpPlot(model_rf3, main = "Variable Importance of measures using MeanDecreaseGini", sort = T, n.var = 10)
# We have got a good accuracy for this model and as per this model the top 10 variables as per their importance are
# 1.READM_30_HOSP_WIDE 2.PSI_90_SAFETY 3.MORT_30_PN 4.H_HSP_RATING_LINEAR_MEAN 5.H_COMP_7_LINEAR_MEAN
# 6.H_RECMND_LINEAR_MEAN 7.H_COMP_3_LINEAR_MEAN 8.MORT_30_HF 9.H_COMP_1_LINEAR_MEAN 10.MORT_30_COPD
# Overall the model is doing good.Data Modeling and Evaluation End
Factor Analysis
We will perform Factor analysis for each of the seven groups.
# install.packages("psych")
# install.packages("Information")library(psych)##
## Attaching package: 'psych'## The following object is masked from 'package:randomForest':
##
## outlier## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha# As per the mentor suggestions we will first calculate the measures for effectiveness group and then create a function to perform the same for all the groups.
# effectiveness scores
effectiveness <- effe_master[, -c(2:8)]
str(effectiveness)## 'data.frame': 4818 obs. of 19 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ CAC_3_score : num NA NA NA NA NA NA NA NA NA NA ...
## $ IMM_2_score : num 0.364 0.53 0.613 0.53 0.197 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num -0.233 -0.099 0.169 -2.11 -2.781 ...
## $ OP_22_score : num -1.201 -0.108 0.438 0.438 0.438 ...
## $ OP_23_score : num NA 0.783 NA NA NA ...
## $ OP_29_score : num NA 0.692 -0.102 -2.627 0.836 ...
## $ OP_30_score : num 0.0711 0.5002 0.3285 -3.4479 0.7148 ...
## $ OP_4_score : num NA 0.546 NA -1.232 NA ...
## $ PC_01_score : num 0.539 0.324 0.539 NA NA ...
## $ STK_4_score : num -1.23 NA NA NA NA ...
## $ STK_5_score : num -0.0249 0.1932 0.1932 0.4114 NA ...
## $ STK_6_score : num 0.446 -0.915 0.198 -3.019 NA ...
## $ STK_8_score : num -0.57 0.618 -0.296 NA NA ...
## $ VTE_1_score : num 0.334 0.178 0.412 0.334 0.489 ...
## $ VTE_2_score : num 0.381 -0.45 -1.282 0.381 NA ...
## $ VTE_3_score : num -0.278 0.819 -0.887 NA NA ...
## $ VTE_5_score : num -0.2536 0.6352 -0.0759 NA NA ...
## $ VTE_6_score : num 0.416 NA 0.416 NA NA ...# remove the Provider.ID column
eff <- effectiveness[, -1]
str(eff)## 'data.frame': 4818 obs. of 18 variables:
## $ CAC_3_score : num NA NA NA NA NA NA NA NA NA NA ...
## $ IMM_2_score : num 0.364 0.53 0.613 0.53 0.197 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num -0.233 -0.099 0.169 -2.11 -2.781 ...
## $ OP_22_score : num -1.201 -0.108 0.438 0.438 0.438 ...
## $ OP_23_score : num NA 0.783 NA NA NA ...
## $ OP_29_score : num NA 0.692 -0.102 -2.627 0.836 ...
## $ OP_30_score : num 0.0711 0.5002 0.3285 -3.4479 0.7148 ...
## $ OP_4_score : num NA 0.546 NA -1.232 NA ...
## $ PC_01_score : num 0.539 0.324 0.539 NA NA ...
## $ STK_4_score : num -1.23 NA NA NA NA ...
## $ STK_5_score : num -0.0249 0.1932 0.1932 0.4114 NA ...
## $ STK_6_score : num 0.446 -0.915 0.198 -3.019 NA ...
## $ STK_8_score : num -0.57 0.618 -0.296 NA NA ...
## $ VTE_1_score : num 0.334 0.178 0.412 0.334 0.489 ...
## $ VTE_2_score : num 0.381 -0.45 -1.282 0.381 NA ...
## $ VTE_3_score : num -0.278 0.819 -0.887 NA NA ...
## $ VTE_5_score : num -0.2536 0.6352 -0.0759 NA NA ...
## $ VTE_6_score : num 0.416 NA 0.416 NA NA ...#scale the data
eff <- as.data.frame(scale(eff))
str(eff)## 'data.frame': 4818 obs. of 18 variables:
## $ CAC_3_score : num NA NA NA NA NA NA NA NA NA NA ...
## $ IMM_2_score : num 0.364 0.53 0.614 0.53 0.197 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num -0.2335 -0.0993 0.1691 -2.1121 -2.783 ...
## $ OP_22_score : num -1.261 -0.117 0.455 0.455 0.455 ...
## $ OP_23_score : num NA 0.783 NA NA NA ...
## $ OP_29_score : num NA 0.692 -0.102 -2.627 0.836 ...
## $ OP_30_score : num 0.0711 0.5002 0.3285 -3.4479 0.7148 ...
## $ OP_4_score : num NA 0.547 NA -1.238 NA ...
## $ PC_01_score : num 0.568 0.339 0.568 NA NA ...
## $ STK_4_score : num -1.23 NA NA NA NA ...
## $ STK_5_score : num -0.0337 0.2084 0.2084 0.4505 NA ...
## $ STK_6_score : num 0.447 -0.921 0.199 -3.035 NA ...
## $ STK_8_score : num -0.573 0.62 -0.298 NA NA ...
## $ VTE_1_score : num 0.334 0.178 0.412 0.334 0.489 ...
## $ VTE_2_score : num 0.398 -0.481 -1.361 0.398 NA ...
## $ VTE_3_score : num -0.279 0.822 -0.892 NA NA ...
## $ VTE_5_score : num -0.2556 0.6375 -0.0769 NA NA ...
## $ VTE_6_score : num 0.469 NA 0.469 NA NA ...# Using psych package, apply factor analysis on eff
# This command was provided by the mentor. We have to use the method=ML (maximum likelihood) and scores="tenBerge"
Eff.fa <- fa(eff, method = "ml", scores = "tenBerge")
# finding the weights
eff_weights <- Eff.fa$weights
eff_weights## MR1
## CAC_3_score -0.042718102
## IMM_2_score 0.098638505
## IMM_3_OP_27_FAC_ADHPCT_score -0.002136510
## OP_22_score 0.069139536
## OP_23_score 0.026341891
## OP_29_score 0.002926853
## OP_30_score 0.043306208
## OP_4_score -0.061446640
## PC_01_score 0.012382577
## STK_4_score 0.045332087
## STK_5_score 0.032026744
## STK_6_score 0.252498944
## STK_8_score 0.158140046
## VTE_1_score 0.570885300
## VTE_2_score -0.003504823
## VTE_3_score 0.056708361
## VTE_5_score 0.041493877
## VTE_6_score 0.053009641sum(eff_weights)## [1] 1.353024# [1] 1.353024
# The sum value is supposed to be 1
eff_weights <- eff_weights / sum(eff_weights)
sum(eff_weights)## [1] 1# CMS says a hospital needs to have at least 3 measures per group
# Rows with attribute values having more than 3 NA's are removed(Removing all such hospitals)
eff$remove <- 0
for (x in 1:nrow(eff)) {
if (sum(!is.na(eff[x,])) <= 3) { eff[x, c("remove")] = 1 }
else { eff[x, c("remove")] = 0 }
}
round(sum(eff$remove) / nrow(eff) * 100, 2)## [1] 22.31# [1] 22.31
# Thus, about 22% hospitals are not valid as per CMS restrictions.
row_num <- which(!eff$remove)
eff <- eff[which(!eff$remove),]
sum(eff$remove)## [1] 0# [1] 0
eff <- eff[, -ncol(eff)]
str(eff)## 'data.frame': 3743 obs. of 18 variables:
## $ CAC_3_score : num NA NA NA NA NA NA NA NA NA NA ...
## $ IMM_2_score : num 0.364 0.53 0.614 0.53 0.197 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num -0.2335 -0.0993 0.1691 -2.1121 -2.783 ...
## $ OP_22_score : num -1.261 -0.117 0.455 0.455 0.455 ...
## $ OP_23_score : num NA 0.783 NA NA NA ...
## $ OP_29_score : num NA 0.692 -0.102 -2.627 0.836 ...
## $ OP_30_score : num 0.0711 0.5002 0.3285 -3.4479 0.7148 ...
## $ OP_4_score : num NA 0.547 NA -1.238 NA ...
## $ PC_01_score : num 0.568 0.339 0.568 NA NA ...
## $ STK_4_score : num -1.23 NA NA NA NA ...
## $ STK_5_score : num -0.0337 0.2084 0.2084 0.4505 NA ...
## $ STK_6_score : num 0.447 -0.921 0.199 -3.035 NA ...
## $ STK_8_score : num -0.573 0.62 -0.298 NA NA ...
## $ VTE_1_score : num 0.334 0.178 0.412 0.334 0.489 ...
## $ VTE_2_score : num 0.398 -0.481 -1.361 0.398 NA ...
## $ VTE_3_score : num -0.279 0.822 -0.892 NA NA ...
## $ VTE_5_score : num -0.2556 0.6375 -0.0769 NA NA ...
## $ VTE_6_score : num 0.469 NA 0.469 NA NA ...# We will replace the NA's with the median values.
median_na <- function(x) {
x[which(is.na(x))] <- median(x, na.rm = T)
return(x)
}
eff <- data.frame(sapply(eff, median_na))
# calculating group scores
head(eff_weights)## MR1
## CAC_3_score -0.031572305
## IMM_2_score 0.072902231
## IMM_3_OP_27_FAC_ADHPCT_score -0.001579062
## OP_22_score 0.051099988
## OP_23_score 0.019468894
## OP_29_score 0.002163193# Multiply each attribute with corresponding weights obtained using Eff.fa$weights
eff <- eff * eff_weights
str(eff)## 'data.frame': 3743 obs. of 18 variables:
## $ CAC_3_score : num -0.011709 0.027037 -0.000586 0.018951 0.00722 ...
## $ IMM_2_score : num 0.014259 -0.016747 0.044739 -0.000838 0.01009 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num -0.00716 -0.00389 -0.00534 -0.15397 0.00439 ...
## $ OP_22_score : num -0.05284 -0.00359 0.01781 -0.01435 0.03314 ...
## $ OP_23_score : num -0.000464 0.032824 0.005498 0.007024 -0.00566 ...
## $ OP_29_score : num 0.155 -0.00179 -0.00426 -0.08056 0.03277 ...
## $ OP_30_score : num 0.008306 0.211053 -0.000851 -0.144511 0.02192 ...
## $ OP_4_score : num 0.06884 0.06398 0.15565 0.00321 0.01546 ...
## $ PC_01_score : num 0.01344 0.063335 0.066362 0.143197 -0.000879 ...
## $ STK_4_score : num -0.04113 0.00753 0.0594 0.0372 0.1343 ...
## $ STK_5_score : num -0.000308 0.006982 0.004933 0.084063 0.052649 ...
## $ STK_6_score : num -0.02032 -0.00842 0.00666 -0.07183 0.0603 ...
## $ STK_8_score : num -0.01834 -0.02818 -0.00272 0.01156 0.00817 ...
## $ VTE_1_score : num 0.000722 0.005707 -0.018689 0.003055 0.016393 ...
## $ VTE_2_score : num 0.00775 -0.00104 -0.04356 -0.01807 0.00364 ...
## $ VTE_3_score : num -0.01428 0.01601 -0.00193 0.01064 -0.0151 ...
## $ VTE_5_score : num 0.000404 0.032576 -0.001498 0.000993 0.014687 ...
## $ VTE_6_score : num 0.03418 -0.00074 0.02396 0.00913 0.00101 ...# For each hospital, calculate average score for effectiveness
# Avg -> summed_score/number_of _measures
str(eff)## 'data.frame': 3743 obs. of 18 variables:
## $ CAC_3_score : num -0.011709 0.027037 -0.000586 0.018951 0.00722 ...
## $ IMM_2_score : num 0.014259 -0.016747 0.044739 -0.000838 0.01009 ...
## $ IMM_3_OP_27_FAC_ADHPCT_score: num -0.00716 -0.00389 -0.00534 -0.15397 0.00439 ...
## $ OP_22_score : num -0.05284 -0.00359 0.01781 -0.01435 0.03314 ...
## $ OP_23_score : num -0.000464 0.032824 0.005498 0.007024 -0.00566 ...
## $ OP_29_score : num 0.155 -0.00179 -0.00426 -0.08056 0.03277 ...
## $ OP_30_score : num 0.008306 0.211053 -0.000851 -0.144511 0.02192 ...
## $ OP_4_score : num 0.06884 0.06398 0.15565 0.00321 0.01546 ...
## $ PC_01_score : num 0.01344 0.063335 0.066362 0.143197 -0.000879 ...
## $ STK_4_score : num -0.04113 0.00753 0.0594 0.0372 0.1343 ...
## $ STK_5_score : num -0.000308 0.006982 0.004933 0.084063 0.052649 ...
## $ STK_6_score : num -0.02032 -0.00842 0.00666 -0.07183 0.0603 ...
## $ STK_8_score : num -0.01834 -0.02818 -0.00272 0.01156 0.00817 ...
## $ VTE_1_score : num 0.000722 0.005707 -0.018689 0.003055 0.016393 ...
## $ VTE_2_score : num 0.00775 -0.00104 -0.04356 -0.01807 0.00364 ...
## $ VTE_3_score : num -0.01428 0.01601 -0.00193 0.01064 -0.0151 ...
## $ VTE_5_score : num 0.000404 0.032576 -0.001498 0.000993 0.014687 ...
## $ VTE_6_score : num 0.03418 -0.00074 0.02396 0.00913 0.00101 ...eff = eff %>% mutate(score = round(rowSums(.) / length(eff), 3))
effectiveness <- effectiveness[row_num,]
effectiveness$score <- eff$score
effectiveness_scores <- effectiveness[, c("Provider.ID", "score")]
str(effectiveness_scores)## 'data.frame': 3743 obs. of 2 variables:
## $ Provider.ID: int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ score : num 0.008 0.022 0.017 -0.009 0.022 -0.088 0.009 0.023 -0.148 -0.01 ...# let us rename the column with appropriate measure name.
names(effectiveness_scores)[2] <- paste("effe_score")
str(effectiveness_scores)## 'data.frame': 3743 obs. of 2 variables:
## $ Provider.ID: int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ effe_score : num 0.008 0.022 0.017 -0.009 0.022 -0.088 0.009 0.023 -0.148 -0.01 ...# Since we need to repeat this process for the rest of the measures let us create a function
function_group_score <- function(data) {
# remove the provide.ID column
df <- data[, -1]
#scale the data
df <- as.data.frame(scale(df))
str(df)
# Using psych package, apply factor analysis on data
# This command was provided by the mentor. We have to use the method=ML (maximum likelihood) and scores="tenBerge"
df.fa <- fa(df, method = "ml", scores = "tenBerge")
# finding the weights
df_weights <- df.fa$weights
sum(df_weights)
df_weights <- df_weights / sum(df_weights)
sum(df_weights)
# CMS says a hospital needs to have at least 3 measures per group
# Rows with attribute values having more than 3 NA's are removed(Removing all such hospitals)
df$remove <- 0
for (i in 1:nrow(df)) {
if (sum(!is.na(df[i,])) <= 3) { df[i, c("remove")] = 1 }
else { df[i, c("remove")] = 0 }
}
row_num <- which(!df$remove)
df <- df[which(!df$remove),]
sum(df$remove)
df <- df[, -ncol(df)]
str(df)
# We will replace the NA's with the median values.
median_na <- function(x) {
x[which(is.na(x))] <- median(x, na.rm = T)
return(x)
}
df <- data.frame(sapply(df, median_na))
# calculating group scores
# Multiply each attribute with corresponding weights obtained using df.fa$weights
df <- df * df_weights
# For each hospital, calculate average score for effectiveness
# Avg -> summed_score/number_of _measures
df = df %>% mutate(score = round(rowSums(.) / length(df), 3))
data <- data[row_num,]
data$score <- df$score
data_scores <- data[, c("Provider.ID", "score")]
# let us rename the column with appropriate measure name.
names(data_scores)[2] <- paste("df_score")
return(data_scores)
}# readmission scores
data <- read_master[, -c(2:8)]
readmission_scores <- function_group_score(data)## 'data.frame': 4818 obs. of 8 variables:
## $ READM_30_AMI_score : num 0.41 0.201 0.829 NA NA ...
## $ READM_30_CABG_score : num -0.619 NA -0.708 NA NA ...
## $ READM_30_COPD_score : num -0.8701 1.5792 0.1571 0.0781 0.6311 ...
## $ READM_30_HF_score : num 0.372 0.036 0.909 0.573 -0.77 ...
## $ READM_30_HIP_KNEE_score : num -0.889 -1.979 -0.708 NA NA ...
## $ READM_30_HOSP_WIDE_score: num 0.218 0.829 0.218 -1.248 -0.148 ...
## $ READM_30_PN_score : num -1.111 0.497 -0.551 -0.132 0.776 ...
## $ READM_30_STK_score : num -0.125 -0.779 0.529 -0.125 NA ...
## 'data.frame': 3816 obs. of 8 variables:
## $ READM_30_AMI_score : num 0.41 0.201 0.829 NA NA ...
## $ READM_30_CABG_score : num -0.619 NA -0.708 NA NA ...
## $ READM_30_COPD_score : num -0.8701 1.5792 0.1571 0.0781 0.6311 ...
## $ READM_30_HF_score : num 0.372 0.036 0.909 0.573 -0.77 ...
## $ READM_30_HIP_KNEE_score : num -0.889 -1.979 -0.708 NA NA ...
## $ READM_30_HOSP_WIDE_score: num 0.218 0.829 0.218 -1.248 -0.148 ...
## $ READM_30_PN_score : num -1.111 0.497 -0.551 -0.132 0.776 ...
## $ READM_30_STK_score : num -0.125 -0.779 0.529 -0.125 NA ...str(readmission_scores)## 'data.frame': 3816 obs. of 2 variables:
## $ Provider.ID: int 10001 10005 10006 10007 10008 10011 10012 10016 10019 10021 ...
## $ df_score : num -0.04 0.002 0.006 -0.01 0.002 0.069 -0.013 0.004 -0.056 0.007 ...names(readmission_scores)[2] <- "radm_score"
head(readmission_scores)## Provider.ID radm_score
## 1 10001 -0.040
## 2 10005 0.002
## 3 10006 0.006
## 4 10007 -0.010
## 5 10008 0.002
## 6 10011 0.069# mortality scores
data <- mort_master[, -c(2:8)]
mortality_scores <- function_group_score(data)## 'data.frame': 4818 obs. of 7 variables:
## $ MORT_30_AMI_score : num 1.25 -1.55 -2.11 NA NA ...
## $ MORT_30_CABG_score : num -1.01 NA -0.89 NA NA ...
## $ MORT_30_COPD_score : num -1.099 0.436 0.887 -1.099 -0.106 ...
## $ MORT_30_HF_score : num -0.166 -2.294 -2.362 -1.539 -0.372 ...
## $ MORT_30_PN_score : num 0.429 -2.114 -0.866 -1.154 0.333 ...
## $ MORT_30_STK_score : num -0.283 -0.343 -1.79 -1.007 NA ...
## $ PSI_4_SURG_COMP_score: num -1.72 -2.3 -3.34 NA NA ...
## 'data.frame': 3480 obs. of 7 variables:
## $ MORT_30_AMI_score : num 1.25 -1.55 -2.11 NA NA ...
## $ MORT_30_CABG_score : num -1.01 NA -0.89 NA NA ...
## $ MORT_30_COPD_score : num -1.099 0.436 0.887 -1.099 -0.106 ...
## $ MORT_30_HF_score : num -0.166 -2.294 -2.362 -1.539 -0.372 ...
## $ MORT_30_PN_score : num 0.429 -2.114 -0.866 -1.154 0.333 ...
## $ MORT_30_STK_score : num -0.283 -0.343 -1.79 -1.007 NA ...
## $ PSI_4_SURG_COMP_score: num -1.72 -2.3 -3.34 NA NA ...str(mortality_scores)## 'data.frame': 3480 obs. of 2 variables:
## $ Provider.ID: int 10001 10005 10006 10007 10008 10011 10012 10016 10019 10021 ...
## $ df_score : num -0.021 -0.164 -0.182 -0.066 0.003 -0.009 -0.162 -0.069 -0.069 0.037 ...names(mortality_scores)[2] <- "mort_score"
head(mortality_scores)## Provider.ID mort_score
## 1 10001 -0.021
## 2 10005 -0.164
## 3 10006 -0.182
## 4 10007 -0.066
## 5 10008 0.003
## 6 10011 -0.009# safety scores
data <- safe_master[, -c(2:8)]
safety_scores <- function_group_score(data)## 'data.frame': 4818 obs. of 8 variables:
## $ COMP_HIP_KNEE_score: num -1.3622 0.0741 -1.3622 NA NA ...
## $ HAI_1_SIR_score : num -2.385 -1.038 0.393 NA NA ...
## $ HAI_2_SIR_score : num -2.1956 0.0472 -0.3802 1.1004 NA ...
## $ HAI_3_SIR_score : num -1.139 0.724 0.82 NA NA ...
## $ HAI_4_SIR_score : num 1.02 NA NA NA NA ...
## $ HAI_5_SIR_score : num 0.65 -0.461 -0.315 NA NA ...
## $ HAI_6_SIR_score : num 0.0559 0.8017 0.5908 1.5924 0.4503 ...
## $ PSI_90_SAFETY_score: num 1.2198 0.2314 -0.1175 0.5802 -0.0593 ...
## 'data.frame': 2954 obs. of 8 variables:
## $ COMP_HIP_KNEE_score: num -1.3622 0.0741 -1.3622 NA -1.1826 ...
## $ HAI_1_SIR_score : num -2.3846 -1.0384 0.3931 NA -0.0358 ...
## $ HAI_2_SIR_score : num -2.1956 0.0472 -0.3802 1.1004 0.188 ...
## $ HAI_3_SIR_score : num -1.139 0.724 0.82 NA -0.584 ...
## $ HAI_4_SIR_score : num 1.02 NA NA NA 1.02 ...
## $ HAI_5_SIR_score : num 0.65 -0.461 -0.315 NA 0.118 ...
## $ HAI_6_SIR_score : num 0.0559 0.8017 0.5908 1.5924 0.2258 ...
## $ PSI_90_SAFETY_score: num 1.22 0.231 -0.117 0.58 -0.583 ...str(safety_scores)## 'data.frame': 2954 obs. of 2 variables:
## $ Provider.ID: int 10001 10005 10006 10007 10011 10012 10016 10019 10021 10023 ...
## $ df_score : num -0.104 -0.005 -0.047 0.051 -0.052 0.06 0.032 0.033 0.04 -0.014 ...names(safety_scores)[2] <- "safety_score"
str(safety_scores)## 'data.frame': 2954 obs. of 2 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10011 10012 10016 10019 10021 10023 ...
## $ safety_score: num -0.104 -0.005 -0.047 0.051 -0.052 0.06 0.032 0.033 0.04 -0.014 ...# experience scores
data <- expe_master[, -c(2:8)]
experience_scores <- function_group_score(data)## 'data.frame': 4818 obs. of 11 variables:
## $ H_CLEAN_LINEAR_SCORE : num -0.855 -1.114 -1.114 0.442 NA ...
## $ H_COMP_1_LINEAR_SCORE : num -0.528 -0.131 -0.131 -0.131 NA ...
## $ H_COMP_2_LINEAR_SCORE : num 0.0404 0.8652 0.8652 1.6899 NA ...
## $ H_COMP_3_LINEAR_SCORE : num -1.203 -0.291 -0.519 0.393 NA ...
## $ H_COMP_4_LINEAR_SCORE : num -0.611 0.168 -0.221 0.557 NA ...
## $ H_COMP_5_LINEAR_SCORE : num -0.417 0.285 -0.183 0.753 NA ...
## $ H_COMP_6_LINEAR_SCORE : num 0.0273 0.3106 -1.1058 -0.256 NA ...
## $ H_COMP_7_LINEAR_SCORE : num 0.166 -0.186 -0.537 0.166 NA ...
## $ H_HSP_RATING_LINEAR_SCORE: num 0.0837 0.3939 -1.1573 -0.5368 NA ...
## $ H_QUIET_LINEAR_SCORE : num 0.97 0.578 0.578 1.755 NA ...
## $ H_RECMND_LINEAR_SCORE : num 0.452 0.222 -0.932 -0.47 NA ...
## 'data.frame': 3508 obs. of 11 variables:
## $ H_CLEAN_LINEAR_SCORE : num -0.855 -1.114 -1.114 0.442 -1.633 ...
## $ H_COMP_1_LINEAR_SCORE : num -0.528 -0.131 -0.131 -0.131 -0.528 ...
## $ H_COMP_2_LINEAR_SCORE : num 0.0404 0.8652 0.8652 1.6899 0.0404 ...
## $ H_COMP_3_LINEAR_SCORE : num -1.203 -0.291 -0.519 0.393 -0.747 ...
## $ H_COMP_4_LINEAR_SCORE : num -0.611 0.168 -0.221 0.557 -0.611 ...
## $ H_COMP_5_LINEAR_SCORE : num -0.417 0.285 -0.183 0.753 -1.119 ...
## $ H_COMP_6_LINEAR_SCORE : num 0.0273 0.3106 -1.1058 -0.256 -0.256 ...
## $ H_COMP_7_LINEAR_SCORE : num 0.166 -0.186 -0.537 0.166 -0.186 ...
## $ H_HSP_RATING_LINEAR_SCORE: num 0.0837 0.3939 -1.1573 -0.5368 -0.2266 ...
## $ H_QUIET_LINEAR_SCORE : num 0.97 0.578 0.578 1.755 0.186 ...
## $ H_RECMND_LINEAR_SCORE : num 0.452 0.222 -0.932 -0.47 -0.24 ...str(experience_scores)## 'data.frame': 3508 obs. of 2 variables:
## $ Provider.ID: int 10001 10005 10006 10007 10011 10012 10016 10019 10021 10022 ...
## $ df_score : num -0.033 -0.017 -0.039 0.039 -0.03 0.017 -0.007 -0.021 0.023 0.069 ...names(experience_scores)[2] <- "expe_score"
str(experience_scores)## 'data.frame': 3508 obs. of 2 variables:
## $ Provider.ID: int 10001 10005 10006 10007 10011 10012 10016 10019 10021 10022 ...
## $ expe_score : num -0.033 -0.017 -0.039 0.039 -0.03 0.017 -0.007 -0.021 0.023 0.069 ...# medical scores
data <- medi_master[, -c(2:8)]
medical_scores <- function_group_score(data)## 'data.frame': 4818 obs. of 5 variables:
## $ OP_10_score: num 0.251 -0.425 -0.278 -1.503 0.526 ...
## $ OP_11_score: num 0.391 -1.217 -0.248 -0.508 NA ...
## $ OP_13_score: num -1.207 -0.308 2.339 NA NA ...
## $ OP_14_score: num 0.208 -0.651 -0.973 NA 1.174 ...
## $ OP_8_score : num 0.301 -0.379 -0.784 NA NA ...
## 'data.frame': 2792 obs. of 5 variables:
## $ OP_10_score: num 0.251 -0.425 -0.278 0.643 0.28 ...
## $ OP_11_score: num 0.391 -1.217 -0.248 -1.251 -1.32 ...
## $ OP_13_score: num -1.207 -0.308 2.339 NA 0.791 ...
## $ OP_14_score: num 0.208 -0.651 -0.973 -0.329 NA ...
## $ OP_8_score : num 0.301 -0.379 -0.784 0.576 NA ...str(medical_scores)## 'data.frame': 2792 obs. of 2 variables:
## $ Provider.ID: int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ df_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...names(medical_scores)[2] <- "medi_score"
str(medical_scores)## 'data.frame': 2792 obs. of 2 variables:
## $ Provider.ID: int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...# timeliness scores
data <- time_master[, -c(2:8)]
timeliness_scores <- function_group_score(data)## 'data.frame': 4818 obs. of 7 variables:
## $ ED_1b_score : num 0.0889 0.3434 0.5979 0.5783 0.9601 ...
## $ ED_2b_score : num 0.51 0.464 0.358 0.51 0.693 ...
## $ OP_18b_score: num -1.279 0.62 0.235 0.572 1.077 ...
## $ OP_20_score : num -2.4285 -0.0502 1.0138 -0.7387 -0.0502 ...
## $ OP_21_score : num -2.592 -0.381 -0.267 -2.082 0.243 ...
## $ OP_3b_score : num NA NA NA NA NA ...
## $ OP_5_score : num NA -0.727 NA 0.25 NA ...
## 'data.frame': 3531 obs. of 7 variables:
## $ ED_1b_score : num 0.0889 0.3434 0.5979 0.5783 0.9601 ...
## $ ED_2b_score : num 0.51 0.464 0.358 0.51 0.693 ...
## $ OP_18b_score: num -1.279 0.62 0.235 0.572 1.077 ...
## $ OP_20_score : num -2.4285 -0.0502 1.0138 -0.7387 -0.0502 ...
## $ OP_21_score : num -2.592 -0.381 -0.267 -2.082 0.243 ...
## $ OP_3b_score : num NA NA NA NA NA ...
## $ OP_5_score : num NA -0.727 NA 0.25 NA ...str(timeliness_scores)## 'data.frame': 3531 obs. of 2 variables:
## $ Provider.ID: int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ df_score : num -0.043 0.071 -0.006 0.111 0.107 -0.229 0.075 -0.048 0.08 -0.042 ...names(timeliness_scores)[2] <- "time_score"
str(timeliness_scores)## 'data.frame': 3531 obs. of 2 variables:
## $ Provider.ID: int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ time_score : num -0.043 0.071 -0.006 0.111 0.107 -0.229 0.075 -0.048 0.08 -0.042 ...# Merge all the group scores into a master data frame group_scores using providerID
merge1 = merge(readmission_scores, mortality_scores, by = "Provider.ID")
merge2 = merge(merge1, safety_scores, by = "Provider.ID")
merge3 = merge(merge2, experience_scores, by = "Provider.ID")
merge4 = merge(merge3, medical_scores, by = "Provider.ID")
merge5 = merge(merge4, timeliness_scores, by = "Provider.ID")
merge6 = merge(merge5, effectiveness_scores, by = "Provider.ID")
group_scores <- merge6##########################################3############ group scores calculation complete ###############################################################
calculating the final scores
Multiply each group_scores with corresponding measure weights given by CMS and sum
them to form final_score=sum(each_column * cms_weights)/7
str(group_scores)## 'data.frame': 2414 obs. of 8 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score: num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...cms_weights <- c(readmission = 0.22, mortality = 0.22, safety = 0.22, experience = 0.22, medical = 0.04, timeliness = 0.04, effectiveness = 0.04)
group_scores[, "final_score"] <-
apply(group_scores[, -c(1, ncol(group_scores))], 1, function(x)
round(sum(x * cms_weights, na.rm = T), 3))## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
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## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object length
## Warning in x * cms_weights: longer object length is not a multiple of
## shorter object lengthstr(group_scores)## 'data.frame': 2414 obs. of 9 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score: num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...head(group_scores)## Provider.ID radm_score mort_score safety_score expe_score medi_score
## 1 10001 -0.040 -0.021 -0.104 -0.033 0.033
## 2 10005 0.002 -0.164 -0.005 -0.017 -0.070
## 3 10006 0.006 -0.182 -0.047 -0.039 0.137
## 4 10011 0.069 -0.009 -0.052 -0.030 -0.080
## 5 10012 -0.013 -0.162 0.060 0.017 -0.112
## 6 10016 0.004 -0.069 0.032 -0.007 -0.271
## time_score effe_score final_score
## 1 -0.043 0.008 -0.046
## 2 0.071 0.022 -0.040
## 3 -0.006 0.017 -0.052
## 4 -0.229 -0.088 -0.014
## 5 0.075 0.009 -0.024
## 6 -0.048 0.023 -0.021# > head(group_scores)
# Provider.ID radm_score mort_score safety_score expe_score medi_score time_score effe_score final_score
# 1 10001 -0.040 -0.021 -0.104 -0.033 0.033 -0.043 0.008 -0.046
# 2 10005 0.002 -0.164 -0.005 -0.017 -0.070 0.071 0.022 -0.040
# 3 10006 0.006 -0.182 -0.047 -0.039 0.137 -0.006 0.017 -0.052
# 4 10011 0.069 -0.009 -0.052 -0.030 -0.080 -0.229 -0.088 -0.014
# 5 10012 -0.013 -0.162 0.060 0.017 -0.112 0.075 0.009 -0.024
# 6 10016 0.004 -0.069 0.032 -0.007 -0.271 -0.048 0.023 -0.021
# >
final_score_df <- group_scores[, c(1, ncol(group_scores))]
summary(final_score_df$final_score)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.261000 -0.025000 0.000000 -0.002328 0.024000 0.250000# > summary(final_score_df$final_score)
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# -0.261000 -0.025000 0.000000 -0.002328 0.024000 0.250000
# Removing the final_score column from group_scores
str(group_scores)## 'data.frame': 2414 obs. of 9 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score: num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...group_scores <- group_scores[, -9]calculating the final scores complete
Unsupervised Modelling using - kmeans clustering
# The values for nstart and ncluster are as per the mentor's suggestions.
score <- kmeans(final_score_df$final_score, 5, nstart = 100)
summary(score)## Length Class Mode
## cluster 2414 -none- numeric
## centers 5 -none- numeric
## totss 1 -none- numeric
## withinss 5 -none- numeric
## tot.withinss 1 -none- numeric
## betweenss 1 -none- numeric
## size 5 -none- numeric
## iter 1 -none- numeric
## ifault 1 -none- numericsummary(factor(score$cluster))## 1 2 3 4 5
## 46 114 421 849 984str(score)## List of 9
## $ cluster : int [1:2414] 3 3 3 5 5 5 5 4 3 5 ...
## $ centers : num [1:5, 1] -0.14433 0.09054 -0.05661 0.02868 -0.00998
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:5] "1" "2" "3" "4" ...
## .. ..$ : NULL
## $ totss : num 4.65
## $ withinss : num [1:5] 0.0824 0.1436 0.1175 0.141 0.1412
## $ tot.withinss: num 0.626
## $ betweenss : num 4.02
## $ size : int [1:5] 46 114 421 849 984
## $ iter : int 2
## $ ifault : int 0
## - attr(*, "class")= chr "kmeans"final_score_df$cluster_id <- score$cluster
f = final_score_df %>%
group_by(cluster_id) %>%
summarise(avg_score = mean(final_score)) %>%
arrange(desc(avg_score))
f## # A tibble: 5 x 2
## cluster_id avg_score
## <int> <dbl>
## 1 2 0.0905
## 2 4 0.0287
## 3 5 -0.00998
## 4 3 -0.0566
## 5 1 -0.144# We notice that after arranging the avg_score column in descending order, the cluster_id values are different.
# we now need to reassign the cluster ratings according to the average rating
# The top most one should be as follows, 5, 4, 3, 2, 1
# We will adjust this by creating a new clusterid column and assign the values to it.
str(f)## Classes 'tbl_df', 'tbl' and 'data.frame': 5 obs. of 2 variables:
## $ cluster_id: int 2 4 5 3 1
## $ avg_score : num 0.09054 0.02868 -0.00998 -0.05661 -0.14433final_score_df$newcluster_id <-
if_else(
final_score_df$cluster_id == f$cluster_id[1],
5,
if_else(
final_score_df$cluster_id == f$cluster_id[2],
4,
if_else(
final_score_df$cluster_id == f$cluster_id[3],
3,
if_else(final_score_df$cluster_id == f$cluster_id[4], 2, 1)
)
)
)
final_score_df %>%
group_by(newcluster_id) %>%
summarise(avg_score = mean(final_score)) %>%
arrange(desc(avg_score))## # A tibble: 5 x 2
## newcluster_id avg_score
## <dbl> <dbl>
## 1 5 0.0905
## 2 4 0.0287
## 3 3 -0.00998
## 4 2 -0.0566
## 5 1 -0.144# # A tibble: 5 x 2
# cluster_id avg_score
# <int> <dbl>
# 5 0.0905
# 4 0.0287
# 3 -0.00972
# 2 -0.0558
# 1 -0.142
str(final_score_df)## 'data.frame': 2414 obs. of 4 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id: num 2 2 2 3 3 3 3 4 2 3 ...final_score_df$newcluster_id <- as.factor(final_score_df$newcluster_id)
summary(final_score_df$newcluster_id)## 1 2 3 4 5
## 46 421 984 849 114# > summary(final_score_df$newcluster_id)
# 1 2 3 4 5
# 48 430 973 849 114
# We see that there are around 114 providers who have top 5 rating and as usual most of the providers are in the median 3 rating.
# Around 849 providers are in top 4 rating.
summary(final_score_df$final_score)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.261000 -0.025000 0.000000 -0.002328 0.024000 0.250000# Visualising the final_score accross all the ratings
head(final_score_df)## Provider.ID final_score cluster_id newcluster_id
## 1 10001 -0.046 3 2
## 2 10005 -0.040 3 2
## 3 10006 -0.052 3 2
## 4 10011 -0.014 5 3
## 5 10012 -0.024 5 3
## 6 10016 -0.021 5 3final_plot <- ggplot(final_score_df, aes(x = newcluster_id, y = final_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("Final Scores") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-0.2, 0.2, 0.05))
final_plot
# Let us plot each group_scores against the final_score_df
# Merging the group_scores with final_scores_df
all_scores <- merge(group_scores, final_score_df, by = "Provider.ID")
# all_scores group by readmission
summary(all_scores$radm_score)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.8400000 -0.0310000 0.0015000 -0.0003761 0.0340000 0.8150000readm_plot <- ggplot(all_scores, aes(x = factor(as.character(newcluster_id)), y = radm_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("Readmission Scores") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-1.0, 1.0, 0.25))
readm_plot
# all_scores group by mortality
summary(all_scores$mort_score)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.280000 -0.047000 0.004000 0.003586 0.055750 0.330000mort_plot <- ggplot(all_scores, aes(x = factor(as.character(newcluster_id)), y = mort_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("Mortality Scores") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-0.2, 0.3, 0.05))
mort_plot
# all_scores group by safety
summary(all_scores$safety_score)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.3210000 -0.0310000 0.0070000 -0.0003123 0.0380000 0.1510000safe_plot <- ggplot(all_scores, aes(x = factor(as.character(newcluster_id)), y = safety_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("safety scores") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-0.3, 0.2, 0.04))
safe_plot
# all_scores group by experience
summary(all_scores$expe_score)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.37200 -0.04900 -0.00700 -0.01326 0.03100 0.21200expe_plot <- ggplot(all_scores, aes(x = factor(as.character(newcluster_id)), y = expe_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("Experience Scores") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-0.4, 0.25, 0.05))
expe_plot
# all_scores group by medical
summary(all_scores$medi_score)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.664000 -0.042000 0.030000 0.007466 0.073000 0.369000medi_plot <- ggplot(all_scores, aes(x = factor(as.character(newcluster_id)), y = medi_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("Medical Scores") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-0.5, 0.2, 0.05))
medi_plot
# all_scores group by timeliness
summary(all_scores$time_score)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.938000 -0.053000 0.016000 -0.008087 0.060000 0.284000time_plot <- ggplot(all_scores, aes(x = factor(as.character(newcluster_id)), y = time_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("Timeliness Scores") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-0.9, 0.3, 0.08))
time_plot
# all_scores group by effectiveness
summary(all_scores$effe_score)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.15300 0.00000 0.01400 0.00808 0.02300 0.05300effe_plot <- ggplot(all_scores, aes(x = factor(as.character(newcluster_id)), y = effe_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("Effectiveness Scores") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-0.2, 0.1, 0.01))
effe_plot
grid_plot2 <- plot_grid(readm_plot, mort_plot, safe_plot, expe_plot, medi_plot, time_plot, effe_plot,
labels = c("Readmission", "Mortality", "Safety", "Experience", "Medical", "Timeliness", "Effectiveness"))
grid_plot2
# Now let us compare the median of all the groups scores with the ratings
median_group_scores <- all_scores[, -c(1, 10)] %>%
group_by(newcluster_id) %>%
summarise_all(.funs = (median), na.rm = T)
median_group_scores## # A tibble: 5 x 9
## newcluster_id radm_score mort_score safety_score expe_score medi_score
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 -0.353 -0.0405 -0.052 -0.086 0.0155
## 2 2 -0.048 -0.046 -0.031 -0.063 0.006
## 3 3 -0.006 -0.008 0.001 -0.021 0.02
## 4 4 0.024 0.03 0.027 0.023 0.044
## 5 5 0.174 0.066 0.0415 0.037 0.056
## # … with 3 more variables: time_score <dbl>, effe_score <dbl>,
## # final_score <dbl># A tibble: 5 x 8
# newcluster_id radm_score mort_score safety_score expe_score medi_score time_score effe_score final_score
# <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 -0.353 -0.0405 -0.052 -0.086 0.0155 -0.0695 0.011 -0.126
# 2 -0.048 -0.046 -0.031 -0.063 0.006 -0.038 0.012 -0.053
# 3 -0.006 -0.008 0.001 -0.021 0.02 0.0165 0.013 -0.009
# 4 0.024 0.03 0.027 0.023 0.044 0.029 0.016 0.027
# 5 0.174 0.066 0.0415 0.037 0.056 0.0365 0.019 0.077
# > >
# We can clearly see the rating increases as the measure scores increase.
mean_group_scores <- all_scores[, -c(1, 10)] %>%
group_by(newcluster_id) %>%
summarise_all(.funs = (mean), na.rm = T)
mean_group_scores## # A tibble: 5 x 9
## newcluster_id radm_score mort_score safety_score expe_score medi_score
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 -0.356 -0.0473 -0.0641 -0.100 -0.0304
## 2 2 -0.0706 -0.0475 -0.0402 -0.0698 -0.0257
## 3 3 -0.0100 -0.00655 -0.00340 -0.0223 -0.000317
## 4 4 0.0353 0.0352 0.0224 0.0222 0.0302
## 5 5 0.220 0.0650 0.0300 0.0449 0.0432
## # … with 3 more variables: time_score <dbl>, effe_score <dbl>,
## # final_score <dbl># newcluster_id radm_score mort_score safety_score expe_score medi_score time_score effe_score final_score
# <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 -0.342 -0.0501 -0.0654 -0.103 -0.0380 -0.104 -0.00231 -0.142
# 2 -0.0711 -0.0455 -0.0393 -0.0689 -0.0244 -0.0621 0.00560 -0.0558
# 3 -0.00925 -0.00680 -0.00325 -0.0220 -0.000215 -0.00609 0.00622 -0.00972
# 4 0.0353 0.0352 0.0224 0.0222 0.0302 0.0186 0.0109 0.0287
# 5 0.220 0.0650 0.0300 0.0449 0.0432 0.0199 0.0170 0.0905
# We can clearly see the rating increases as the measure scores increase.Validation of Star Ratings
# master_data_y is the hospital_ratings dataset we will use it here.
str(master_data_y)## 'data.frame': 4818 obs. of 2 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ Hospital.overall.rating: int 3 3 2 3 3 2 3 3 NA 2 ...# Let us convert the ratings column to factor
master_data_y$Hospital.overall.rating <- factor(master_data_y$Hospital.overall.rating)
# merging the final_score_df with the master_data_y using Provider.ID
final_score_df <- merge(final_score_df, master_data_y, by = "Provider.ID")
str(final_score_df)## 'data.frame': 2414 obs. of 5 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id : Factor w/ 5 levels "1","2","3","4",..: 2 2 2 3 3 3 3 4 2 3 ...
## $ Hospital.overall.rating: Factor w/ 5 levels "1","2","3","4",..: 3 3 2 2 3 3 2 4 3 3 ...summary(final_score_df$newcluster_id)## 1 2 3 4 5
## 46 421 984 849 114# Accuracy is determined by comparing the overall ratings of CMS(general.csv) by creating the confusion matrix.
final = table(final_score_df$newcluster_id, final_score_df$Hospital.overall.rating)
final##
## 1 2 3 4 5
## 1 21 21 2 2 0
## 2 78 248 89 6 0
## 3 2 249 622 110 1
## 4 0 19 377 422 31
## 5 0 1 14 73 26conf_matrix4 <- confusionMatrix(final)
conf_matrix4## Confusion Matrix and Statistics
##
##
## 1 2 3 4 5
## 1 21 21 2 2 0
## 2 78 248 89 6 0
## 3 2 249 622 110 1
## 4 0 19 377 422 31
## 5 0 1 14 73 26
##
## Overall Statistics
##
## Accuracy : 0.5547
## 95% CI : (0.5346, 0.5746)
## No Information Rate : 0.4573
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.3484
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
## Sensitivity 0.207921 0.4610 0.5634 0.6884 0.44828
## Specificity 0.989192 0.9078 0.7237 0.7629 0.96265
## Pos Pred Value 0.456522 0.5891 0.6321 0.4971 0.22807
## Neg Pred Value 0.966216 0.8545 0.6629 0.8780 0.98609
## Prevalence 0.041839 0.2229 0.4573 0.2539 0.02403
## Detection Rate 0.008699 0.1027 0.2577 0.1748 0.01077
## Detection Prevalence 0.019056 0.1744 0.4076 0.3517 0.04722
## Balanced Accuracy 0.598556 0.6844 0.6435 0.7257 0.70546# Confusion Matrix and Statistics
#
#
# 1 2 3 4 5
# 1 22 22 2 2 0
# 2 77 253 93 7 0
# 3 2 243 618 109 1
# 4 0 19 377 422 31
# 5 0 1 14 73 26
#
# Overall Statistics
#
# Accuracy : 0.5555
# 95% CI : (0.5354, 0.5755)
# No Information Rate : 0.4573
# P-Value [Acc > NIR] : < 2.2e-16
#
# Kappa : 0.3508
# Mcnemar's Test P-Value : NA
#
# Statistics by Class:
#
# Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
# Sensitivity 0.217822 0.4703 0.5598 0.6884 0.44828
# Specificity 0.988759 0.9057 0.7290 0.7629 0.96265
# Pos Pred Value 0.458333 0.5884 0.6351 0.4971 0.22807
# Neg Pred Value 0.966610 0.8564 0.6627 0.8780 0.98609
# Prevalence 0.041839 0.2229 0.4573 0.2539 0.02403
# Detection Rate 0.009114 0.1048 0.2560 0.1748 0.01077
# Detection Prevalence 0.019884 0.1781 0.4031 0.3517 0.04722
# Balanced Accuracy 0.603290 0.6880 0.6444 0.7257 0.70546
# Accuracy : 55.55%
# We see the accuracy is between 50 to 60%, and the model has good scope as it is not overfitting the data.End of Factor Analysis and Un-supervised Clustering
Provider Analysis for Evanston Hospital
# Using the group_scores dataset
# all_scores
str(all_scores)## 'data.frame': 2414 obs. of 11 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score : num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id: Factor w/ 5 levels "1","2","3","4",..: 2 2 2 3 3 3 3 4 2 3 ...# Provider: Evanston Hospital Provider.ID = 140010
# Check the group scores for the provider.ID = 140010
provider_140010_gp_scores <- all_scores[which(all_scores$Provider.ID == 140010),]
provider_140010_gp_scores## Provider.ID radm_score mort_score safety_score expe_score medi_score
## 637 140010 0.019 0.278 -0.022 -0.002 -0.013
## time_score effe_score final_score cluster_id newcluster_id
## 637 0.068 0.015 0.063 2 5# Provider.ID radm_score mort_score safety_score expe_score medi_score time_score effe_score final_score cluster_id newcluster_id
# 637 140010 0.019 0.278 -0.022 -0.002 -0.013 0.068 0.015 0.063 3 5
str(master_data_y)## 'data.frame': 4818 obs. of 2 variables:
## $ Provider.ID : int 10001 10005 10006 10007 10008 10011 10012 10016 10018 10019 ...
## $ Hospital.overall.rating: Factor w/ 5 levels "1","2","3","4",..: 3 3 2 3 3 2 3 3 NA 2 ...provider_140010_cms_rating = master_data_y[which(master_data_y$Provider.ID == 140010),]
str(provider_140010_cms_rating)## 'data.frame': 1 obs. of 2 variables:
## $ Provider.ID : int 140010
## $ Hospital.overall.rating: Factor w/ 5 levels "1","2","3","4",..: 3as.numeric(provider_140010_cms_rating$Hospital.overall.rating)## [1] 3# [1] 3
# The current rating of the provider is 3 in the hospital general info provided by CMS.
as.numeric(provider_140010_gp_scores$newcluster_id)## [1] 5# [1] 5
# The rating as per the kmeans model is 5, which is a +2 error
value = provider_140010_gp_scores$final_score * 100
value## [1] 6.3# Let us compare the provider's final score with the overall scores.
summary(all_scores$final_score)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.261000 -0.025000 0.000000 -0.002328 0.024000 0.250000final_plot <- ggplot(all_scores, aes(x = newcluster_id, y = final_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("Final Scores with the Provider Final Score") +
theme_light() +
geom_hline(aes(yintercept = value), color = "blue") +
geom_text(aes(0, value, label = value, vjust = -1, hjust = -1)) +
scale_y_continuous(breaks = 100 * seq(-0.2, 0.2, 0.05))
final_plot
# let us see how the provider score looks in all the group measures.
f1 <- provider_140010_gp_scores$final_score * 100
v1 <- provider_140010_gp_scores$radm_score * 100
v2 <- provider_140010_gp_scores$mort_score * 100
v3 <- provider_140010_gp_scores$safety_score * 100
v4 <- provider_140010_gp_scores$expe_score * 100
v5 <- provider_140010_gp_scores$medi_score * 100
v6 <- provider_140010_gp_scores$time_score * 100
v7 <- provider_140010_gp_scores$effe_score * 100
f <- final_plot + geom_hline(yintercept = f1, col = "black")
r <- readm_plot +
geom_hline(yintercept = v1, col = "maroon") +
geom_text(aes(0, v1, label = v1, vjust = -1, hjust = -1))
m <- mort_plot +
geom_hline(yintercept = v2, col = "maroon") +
geom_text(aes(0, v2, label = v2, vjust = 1, hjust = -1))
s <- safe_plot +
geom_hline(yintercept = v3, col = "maroon") +
geom_text(aes(0, v3, label = v3, vjust = -1, hjust = -1))
ex <- expe_plot +
geom_hline(yintercept = v4, col = "maroon") +
geom_text(aes(0, v4, label = v4, vjust = -1, hjust = -1))
md <- medi_plot +
geom_hline(yintercept = v5, col = "maroon") +
geom_text(aes(0, v5, label = v5, vjust = -1, hjust = -1))
t <- time_plot +
geom_hline(yintercept = v6, col = "maroon") +
geom_text(aes(0, v6, label = v6, vjust = -1, hjust = -1))
ef <- effe_plot +
geom_hline(yintercept = v7, col = "maroon") +
geom_text(aes(0, v7, label = v7, vjust = -1, hjust = -1))# Let us plot all the above graphs with the provider's y intercept for each group's score.
grid_plot3 <- plot_grid(f, r, m, s, ex, md, t, ef,
labels = c('Final', 'Readmission', 'Mortality', 'Safety', 'Experience', 'Medical', 'Timeliness', 'Effectiveness'),
ncol = 2, nrow = 4)
grid_plot3
# On comparing the average scores of groups with the provider's score, we find that:
# We see that the final score of the provider is above final score of rating 4.
# 1. The final score is higher than the average score for rating=4 but lower than for rating=5
# 2. Readmissions score is between avg of ratings 3 and 4
# 3. Mortality score is better than average for rating=5
# 4. Safety score is between the avg score for rating = 2 and 1
# 5. Experience score is above the average of ratings 3 and below the average of 4
# 6. Medical score is very bad compared to all the average ratings scores.
# 7. Timeliness score is better than average for rating 5
# 8. Effectiveness score is better than average for rating for 4 and below the average of rating 5
# We can observe that the overall ratings accross Final, Readminssion, Mortality, Timeliness and effectiveness
# is very good compared.
# The areas of improvement are needed in Safety, Experience and Medical.
# Though medical has very less weightage, let us focus on drilling down Safety, Experience and Medical scores,
# which have 22% weightage.
# Now let us view how the median values of each group is looking with respect to the providers scores.
str(all_scores)## 'data.frame': 2414 obs. of 11 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score : num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id: Factor w/ 5 levels "1","2","3","4",..: 2 2 2 3 3 3 3 4 2 3 ...all_median_scores <- round(summarise_all(all_scores[, -c(1, 10, 11)], .funs = (median), na.rm = T), 3)
all_median_scores## radm_score mort_score safety_score expe_score medi_score time_score
## 1 0.002 0.004 0.007 -0.007 0.03 0.016
## effe_score final_score
## 1 0.014 0all_median_scores <- cbind(id = "median_score", all_median_scores)
new_prov <- provider_140010_gp_scores[, -c(1, 10, 11)]
new_prov <- cbind(id = "provider_score", new_prov)
all_median_scores <- rbind(all_median_scores, new_prov)
all_median_scores## id radm_score mort_score safety_score expe_score
## 1 median_score 0.002 0.004 0.007 -0.007
## 637 provider_score 0.019 0.278 -0.022 -0.002
## medi_score time_score effe_score final_score
## 1 0.030 0.016 0.014 0.000
## 637 -0.013 0.068 0.015 0.063# id radm_score mort_score safety_score exp_score medi_score time_score effe_score final_score
# 1 median_score 0.002 0.004 0.007 -0.007 0.030 0.016 0.014 0.000
# 637 provider_score 0.019 0.278 -0.022 -0.002 -0.013 0.068 0.015 0.063
str(all_scores)## 'data.frame': 2414 obs. of 11 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score : num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id: Factor w/ 5 levels "1","2","3","4",..: 2 2 2 3 3 3 3 4 2 3 ...str(all_scores)## 'data.frame': 2414 obs. of 11 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score : num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id: Factor w/ 5 levels "1","2","3","4",..: 2 2 2 3 3 3 3 4 2 3 ...all_mean_scores <- round(summarise_all(all_scores[, -c(1, 10, 11)], .funs = (mean), na.rm = T), 3)
all_mean_scores## radm_score mort_score safety_score expe_score medi_score time_score
## 1 0 0.004 0 -0.013 0.007 -0.008
## effe_score final_score
## 1 0.008 -0.002all_mean_scores <- cbind(id = "mean_score", all_mean_scores)
new_prov <- provider_140010_gp_scores[, -c(1, 10, 11)]
new_prov <- cbind(id = "provider_score", new_prov)
all_mean_scores <- rbind(all_mean_scores, new_prov)
all_mean_scores## id radm_score mort_score safety_score expe_score
## 1 mean_score 0.000 0.004 0.000 -0.013
## 637 provider_score 0.019 0.278 -0.022 -0.002
## medi_score time_score effe_score final_score
## 1 0.007 -0.008 0.008 -0.002
## 637 -0.013 0.068 0.015 0.063# id radm_score mort_score safety_score expe_score medi_score time_score effe_score final_score
# mean_score 0.000 0.004 0.000 -0.013 0.007 -0.008 0.008 -0.002
# provider_score 0.019 0.278 -0.022 -0.002 -0.013 0.068 0.015 0.063
# The safety scores are lower than the overall average score, experience score is a bit okay with respect
# to the overall average, but the medical scores are very low.
# let us start the drill down in this order- Safety, Experience and Medical
# Let us plot the safety scores graph and identify the concern points1.Safety
#———–#
# we will plot the provider score in the y intercept.
h1 <- provider_140010_gp_scores$safety_score * 100
safe_plot +
geom_hline(yintercept = h1, col = "maroon") +
geom_text(aes(0, h1, label = h1, vjust = -1, hjust = -1))
safety_scores <- safe_master[, -c(2:8)]
summary(safety_scores)## Provider.ID COMP_HIP_KNEE_score HAI_1_SIR_score HAI_2_SIR_score
## Min. : 10001 Min. :-4.2624 Min. :-7.5390 Min. :-5.7477
## 1st Qu.:140185 1st Qu.:-0.6403 1st Qu.:-0.4020 1st Qu.:-0.4872
## Median :260037 Median : 0.0745 Median : 0.1505 Median : 0.1293
## Mean :267984 Mean : 0.0007 Mean : 0.0016 Mean : 0.0051
## 3rd Qu.:390209 3rd Qu.: 0.6105 3rd Qu.: 0.6539 3rd Qu.: 0.7788
## Max. :670112 Max. : 2.5012 Max. : 1.0575 Max. : 1.0540
## NA's :2104 NA's :2443 NA's :1929
## HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score HAI_6_SIR_score
## Min. :-4.5045 Min. :-4.405 Min. :-5.8483 Min. :-4.7375
## 1st Qu.:-0.5577 1st Qu.:-0.547 1st Qu.:-0.4477 1st Qu.:-0.5783
## Median : 0.1646 Median : 0.201 Median : 0.1887 Median : 0.0363
## Mean : 0.0006 Mean : 0.000 Mean : 0.0011 Mean : 0.0010
## 3rd Qu.: 0.7073 3rd Qu.: 1.018 3rd Qu.: 0.6400 3rd Qu.: 0.6300
## Max. : 1.2061 Max. : 1.018 Max. : 1.1289 Max. : 1.5849
## NA's :2775 NA's :3962 NA's :2988 NA's :1546
## PSI_90_SAFETY_score
## Min. :-6.0575
## 1st Qu.:-0.4039
## Median : 0.0863
## Mean : 0.0009
## 3rd Qu.: 0.5764
## Max. : 2.3064
## NA's :1594# Replace the NA's with median values
median_na <- function(x) {
x[which(is.na(x))] <- median(x, na.rm = T)
return(x)
}
safety_scores[, 2:ncol(safety_scores)] <- sapply(safety_scores[, -1], median_na)
summary(safety_scores)## Provider.ID COMP_HIP_KNEE_score HAI_1_SIR_score
## Min. : 10001 Min. :-4.26238 Min. :-7.53897
## 1st Qu.:140185 1st Qu.:-0.10422 1st Qu.: 0.15046
## Median :260037 Median : 0.07446 Median : 0.15046
## Mean :267984 Mean : 0.03293 Mean : 0.07709
## 3rd Qu.:390209 3rd Qu.: 0.25314 3rd Qu.: 0.15046
## Max. :670112 Max. : 2.50121 Max. : 1.05750
## HAI_2_SIR_score HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score
## Min. :-5.74767 Min. :-4.50448 Min. :-4.4049 Min. :-5.8483
## 1st Qu.:-0.05786 1st Qu.: 0.16460 1st Qu.: 0.2012 1st Qu.: 0.1887
## Median : 0.12929 Median : 0.16460 Median : 0.2012 Median : 0.1887
## Mean : 0.05481 Mean : 0.09507 Mean : 0.1655 Mean : 0.1174
## 3rd Qu.: 0.31644 3rd Qu.: 0.16460 3rd Qu.: 0.2012 3rd Qu.: 0.1887
## Max. : 1.05402 Max. : 1.20615 Max. : 1.0177 Max. : 1.1289
## HAI_6_SIR_score PSI_90_SAFETY_score
## Min. :-4.73747 Min. :-6.05751
## 1st Qu.:-0.27104 1st Qu.:-0.05792
## Median : 0.03625 Median : 0.08625
## Mean : 0.01234 Mean : 0.02916
## 3rd Qu.: 0.31830 3rd Qu.: 0.34575
## Max. : 1.58486 Max. : 2.30644# The safety scores for the provider
provider_safe_scores <- round(safety_scores[which(safety_scores$Provider.ID == 140010),], 3)
provider_safe_scores## Provider.ID COMP_HIP_KNEE_score HAI_1_SIR_score HAI_2_SIR_score
## 1125 140010 -0.462 -0.112 -0.214
## HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score HAI_6_SIR_score
## 1125 -0.244 1.018 0.191 -0.291
## PSI_90_SAFETY_score
## 1125 -3.23# Provider.ID COMP_HIP_KNEE_score HAI_1_SIR_score HAI_2_SIR_score HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score HAI_6_SIR_score PSI_90_SAFETY_score
# 140010 -0.462 -0.112 -0.214 -0.244 1.018 0.191 -0.291 -3.23
# Let us calculate the average scores and compare them
average_safety_scores <- round(summarise_all(safety_scores[, -1], .funs = (mean), na.rm = T), 3)
average_safety_scores <- cbind(id = "mean_score", average_safety_scores)
average_safety_scores## id COMP_HIP_KNEE_score HAI_1_SIR_score HAI_2_SIR_score
## 1 mean_score 0.033 0.077 0.055
## HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score HAI_6_SIR_score
## 1 0.095 0.166 0.117 0.012
## PSI_90_SAFETY_score
## 1 0.029# id COMP_HIP_KNEE_score HAI_1_SIR_score HAI_2_SIR_score HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score HAI_6_SIR_score PSI_90_SAFETY_score
# mean_score 0.033 0.077 0.055 0.095 0.166 0.117 0.012 0.029
prov_s <- round(safety_scores[which(safety_scores$Provider.ID == 140010),], 3)
prov_s <- cbind(id = "prov_s_score", prov_s[, -1])
prov_s## id COMP_HIP_KNEE_score HAI_1_SIR_score HAI_2_SIR_score
## 1125 prov_s_score -0.462 -0.112 -0.214
## HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score HAI_6_SIR_score
## 1125 -0.244 1.018 0.191 -0.291
## PSI_90_SAFETY_score
## 1125 -3.23# id Provider.ID COMP_HIP_KNEE_score HAI_1_SIR_score HAI_2_SIR_score HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score HAI_6_SIR_score PSI_90_SAFETY_score
# prov_s_score 140010 -0.462 -0.112 -0.214 -0.244 1.018 0.191 -0.291 -3.23
average_safety_scores <- rbind(average_safety_scores, prov_s)
average_safety_scores## id COMP_HIP_KNEE_score HAI_1_SIR_score HAI_2_SIR_score
## 1 mean_score 0.033 0.077 0.055
## 1125 prov_s_score -0.462 -0.112 -0.214
## HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score HAI_6_SIR_score
## 1 0.095 0.166 0.117 0.012
## 1125 -0.244 1.018 0.191 -0.291
## PSI_90_SAFETY_score
## 1 0.029
## 1125 -3.230# id COMP_HIP_KNEE_score HAI_1_SIR_score HAI_2_SIR_score HAI_3_SIR_score HAI_4_SIR_score HAI_5_SIR_score HAI_6_SIR_score PSI_90_SAFETY_score
# mean_score 0.033 0.077 0.055 0.095 0.166 0.117 0.012 0.029
# prov_s_score -0.462 -0.112 -0.214 -0.244 1.018 0.191 -0.291 -3.230
# Except the HAI_4_SIR and HAI_5_SIR measures all the other measures are way below the average scores.
# Let us visualize how these low score measures are performing with respect to the ratings
# merging safety scores with all_scores
str(all_scores)## 'data.frame': 2414 obs. of 11 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score : num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id: Factor w/ 5 levels "1","2","3","4",..: 2 2 2 3 3 3 3 4 2 3 ...safety_scores <- merge(safety_scores, all_scores, by = "Provider.ID")
str(safety_scores)## 'data.frame': 2414 obs. of 19 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ COMP_HIP_KNEE_score: num -1.355 0.0745 -1.355 -1.1763 0.4318 ...
## $ HAI_1_SIR_score : num -2.3512 -1.023 0.3895 -0.0337 0.1505 ...
## $ HAI_2_SIR_score : num -2.088 0.05 -0.357 0.184 1.054 ...
## $ HAI_3_SIR_score : num -1.135 0.723 0.818 -0.582 0.165 ...
## $ HAI_4_SIR_score : num 1.018 0.201 0.201 1.018 0.201 ...
## $ HAI_5_SIR_score : num 0.647 -0.457 -0.312 0.118 0.189 ...
## $ HAI_6_SIR_score : num 0.0566 0.7984 0.5887 0.2256 1.148 ...
## $ PSI_90_SAFETY_score: num 1.2108 0.2304 -0.1156 -0.5769 -0.0579 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score : num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id : Factor w/ 5 levels "1","2","3","4",..: 2 2 2 3 3 3 3 4 2 3 ...# Measure: COMP_HIP_KNEE_score
summary(safety_scores$COMP_HIP_KNEE_score)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -4.26238 -0.46158 0.07446 -0.01261 0.61050 2.50121s1 <- prov_s$COMP_HIP_KNEE_score * 100
CHK_plot <- ggplot(safety_scores, aes(x = factor(as.character(newcluster_id)), y = COMP_HIP_KNEE_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("COMP_HIP_KNEE_score") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-4, 2.5, 0.5))
safe_chk_p <- CHK_plot +
geom_hline(yintercept = s1, col = "black") +
geom_text(aes(0, s1, label = s1, vjust = -1, hjust = -1))
safe_chk_p
# The provider's score for COMP_HIP_KNEE_score is way below the average scores for all the ratings.
# This is a cause of concern, the provider has to improve on this score to improve their ratings.# Measure: HAI_1_SIR_score
summary(safety_scores$HAI_1_SIR_score)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -7.53897 -0.25261 0.15046 0.05063 0.55402 1.05750s2 <- prov_s$HAI_1_SIR_score * 100
HAI1_plot <- ggplot(safety_scores, aes(x = factor(as.character(newcluster_id)), y = HAI_1_SIR_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("HAI_1_SIR_score") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-7, 1, 0.5))
safe_hai1_p <- HAI1_plot +
geom_hline(yintercept = s2, col = "black") +
geom_text(aes(0, s2, label = s2, vjust = -1, hjust = -1))
safe_hai1_p
# HAI_1_SIR score of the provider is lower than the average scores for ratings 3, 4, 5 and
# just below the average score for rating 2.
# Measure: HAI_2_SIR_score
summary(safety_scores$HAI_2_SIR_score)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -5.74767 -0.47399 0.10837 -0.02428 0.61588 1.05402s3 <- prov_s$HAI_2_SIR_score * 100
HAI2_plot <- ggplot(safety_scores, aes(x = factor(as.character(newcluster_id)), y = HAI_2_SIR_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("HAI_2_SIR_score") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-5, 1, 0.5))
safe_hai2_p <- HAI2_plot +
geom_hline(yintercept = s3, col = "black") +
geom_text(aes(0, s3, label = s3, vjust = -1, hjust = -1))
safe_hai2_p
# HAI_2_SIR score of the provider is lower than the average scores for ratings 3, 4, 5 and
# just below the average scores for ratings 1 & 2.
# Measure: HAI_3_SIR_score
summary(safety_scores$HAI_3_SIR_score)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -4.50448 -0.29764 0.16460 0.03225 0.51700 1.20615s4 <- prov_s$HAI_3_SIR_score * 100
HAI3_plot <- ggplot(safety_scores, aes(x = factor(as.character(newcluster_id)), y = HAI_3_SIR_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("HAI_3_SIR_score") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-5, 1, 0.5))
safe_hai3_p <- HAI3_plot +
geom_hline(yintercept = s4, col = "black") +
geom_text(aes(0, s4, label = s4, vjust = -1, hjust = -1))
safe_hai3_p
# HAI_3_SIR score of the provider is way below the average scores for all the ratings.
# This is a cause of concern, the provider has to improve on this score to improve their ratings.
# Measure: HAI_6_SIR_score
summary(safety_scores$HAI_6_SIR_score)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -4.73747 -0.61863 -0.08511 -0.13336 0.41540 1.58486s5 <- prov_s$HAI_6_SIR_score * 100
HAI6_plot <- ggplot(safety_scores, aes(x = factor(as.character(newcluster_id)), y = HAI_6_SIR_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("HAI_6_SIR_score") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-5, 1, 0.5))
safe_hai6_p <- HAI6_plot +
geom_hline(yintercept = s5, col = "black") +
geom_text(aes(0, s5, label = s5, vjust = -1, hjust = -1))
safe_hai6_p
# HAI_6_SIR score of the provider is below the average scores for all the ratings.
# This is a cause of concern, the provider has to improve on this score to improve their ratings.
# Measure: PSI_90_SAFETY_score
summary(safety_scores$PSI_90_SAFETY_score)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -6.0575 -0.5193 0.1151 -0.0289 0.6918 2.3064s6 <- prov_s$PSI_90_SAFETY_score * 100
PSI90_plot <- ggplot(safety_scores, aes(x = factor(as.character(newcluster_id)), y = PSI_90_SAFETY_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("PSI_90_SAFETY_score") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-5, 2, 0.75))
safe_psi90_p <- PSI90_plot +
geom_hline(yintercept = s6, col = "black") +
geom_text(aes(0, s6, label = s6, vjust = -1, hjust = -1))
safe_psi90_p
# PSI_90_SAFETY score of the provider is way below the average scores for all the ratings.
# The provider is doing poorly in this measure.
# This is a cause of concern, the provider has to improve on this score to improve their ratings.
# Let us plot all the safety graphs together.
grid_plot4 <- plot_grid(safe_chk_p, safe_hai1_p, safe_hai2_p, safe_hai3_p, safe_hai6_p, safe_psi90_p,
labels = c('COMP_HIP_KNEE_score', 'HAI_1_SIR_score', 'HAI_2_SIR_score', 'HAI_3_SIR_score', 'HAI_6_SIR_score', 'PSI_90_SAFETY_score'),
ncol = 2, nrow = 3)
grid_plot4
2.Experience
#————–#
# we will plot the provider score in the y intercept.
h2 <- provider_140010_gp_scores$expe_score * 100
expe_plot +
geom_hline(yintercept = h2, col = "maroon") +
geom_text(aes(0, h2, label = h2, vjust = -1, hjust = -1))
expe_scores <- expe_master[, -c(2:8)]
summary(expe_scores)## Provider.ID H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE
## Min. : 10001 Min. :-3.1829 Min. :-4.8617
## 1st Qu.:140185 1st Qu.:-0.5938 1st Qu.:-0.5235
## Median :260037 Median :-0.0759 Median : 0.2652
## Mean :267984 Mean : 0.0003 Mean : 0.0007
## 3rd Qu.:390209 3rd Qu.: 0.7008 3rd Qu.: 0.6596
## Max. :670112 Max. : 2.7721 Max. : 2.6314
## NA's :1310 NA's :1310
## H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE
## Min. :-4.4601 Min. :-4.3050 Min. :-4.8584
## 1st Qu.:-0.7772 1st Qu.:-0.5175 1st Qu.:-0.6064
## Median : 0.0412 Median :-0.0626 Median : 0.1667
## Mean : 0.0010 Mean : 0.0001 Mean : 0.0000
## 3rd Qu.: 0.4504 3rd Qu.: 0.6198 3rd Qu.: 0.5533
## Max. : 2.9056 Max. : 2.6670 Max. : 3.0972
## NA's :1310 NA's :1310 NA's :1310
## H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE H_COMP_7_LINEAR_SCORE
## Min. :-3.9139 Min. :-4.5537 Min. :-4.0139
## 1st Qu.:-0.6485 1st Qu.:-0.5344 1st Qu.:-0.5316
## Median : 0.0513 Median : 0.0276 Median : 0.1649
## Mean : 0.0001 Mean : 0.0005 Mean : 0.0005
## 3rd Qu.: 0.5178 3rd Qu.: 0.5897 3rd Qu.: 0.5131
## Max. : 3.2190 Max. : 2.2758 Max. : 3.6472
## NA's :1310 NA's :1310 NA's :1310
## H_HSP_RATING_LINEAR_SCORE H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
## Min. :-4.5255 Min. :-3.4634 Min. :-4.5030
## 1st Qu.:-0.5304 1st Qu.:-0.5967 1st Qu.:-0.6938
## Median : 0.0842 Median :-0.0098 Median : 0.2213
## Mean : 0.0013 Mean : 0.0004 Mean : 0.0015
## 3rd Qu.: 0.6988 3rd Qu.: 0.7729 3rd Qu.: 0.6788
## Max. : 2.7213 Max. : 2.7295 Max. : 2.5089
## NA's :1310 NA's :1310 NA's :1310# Replace the NA's with median values
expe_scores[, 2:ncol(expe_scores)] <- sapply(expe_scores[, -1], median_na)
summary(expe_scores)## Provider.ID H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE
## Min. : 10001 Min. :-3.18290 Min. :-4.8617
## 1st Qu.:140185 1st Qu.:-0.33486 1st Qu.:-0.1292
## Median :260037 Median :-0.07595 Median : 0.2652
## Mean :267984 Mean :-0.02043 Mean : 0.0726
## 3rd Qu.:390209 3rd Qu.: 0.44188 3rd Qu.: 0.2652
## Max. :670112 Max. : 2.77210 Max. : 2.6314
## H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE
## Min. :-4.46010 Min. :-4.30501 Min. :-4.85845
## 1st Qu.:-0.36803 1st Qu.:-0.29003 1st Qu.:-0.21983
## Median : 0.04118 Median :-0.06257 Median : 0.16672
## Mean : 0.01196 Mean :-0.01693 Mean : 0.04532
## 3rd Qu.: 0.45038 3rd Qu.: 0.39235 3rd Qu.: 0.16672
## Max. : 2.90563 Max. : 2.66695 Max. : 3.09723
## H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE H_COMP_7_LINEAR_SCORE
## Min. :-3.91389 Min. :-4.553676 Min. :-4.01386
## 1st Qu.:-0.41523 1st Qu.:-0.253380 1st Qu.:-0.18335
## Median : 0.05126 Median : 0.027637 Median : 0.16488
## Mean : 0.01403 Mean : 0.007893 Mean : 0.04519
## 3rd Qu.: 0.28451 3rd Qu.: 0.308654 3rd Qu.: 0.51311
## Max. : 3.21899 Max. : 2.275770 Max. : 3.64717
## H_HSP_RATING_LINEAR_SCORE H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
## Min. :-4.52553 Min. :-3.463444 Min. :-4.50301
## 1st Qu.:-0.22313 1st Qu.:-0.401084 1st Qu.:-0.23626
## Median : 0.08419 Median :-0.009761 Median : 0.22126
## Mean : 0.02384 Mean :-0.002380 Mean : 0.06124
## 3rd Qu.: 0.39150 3rd Qu.: 0.381562 3rd Qu.: 0.45003
## Max. : 2.72131 Max. : 2.729500 Max. : 2.50890# The experience scores for the provider
provider_expe_scores <- round(expe_scores[which(expe_scores$Provider.ID == 140010),], 3)
provider_expe_scores## Provider.ID H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE
## 1125 140010 0.183 -0.129
## H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE
## 1125 -0.368 -0.29 -0.22
## H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE H_COMP_7_LINEAR_SCORE
## 1125 -0.648 -0.815 -0.183
## H_HSP_RATING_LINEAR_SCORE H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
## 1125 0.084 -0.205 0.45# Provider.ID H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE
# 140010 0.183 -0.129 -0.368 -0.29 -0.22 -0.648 -0.815
# H_COMP_7_LINEAR_SCORE H_HSP_RATING_LINEAR_SCORE H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
# -0.183 0.084 -0.205 0.45
# Let us calculate the average scores and compare them
average_expe_scores <- round(summarise_all(expe_scores[, -1], .funs = (mean), na.rm = T), 3)
average_expe_scores <- cbind(id = "mean_score", average_expe_scores)
average_expe_scores## id H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE
## 1 mean_score -0.02 0.073
## H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE
## 1 0.012 -0.017 0.045
## H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE H_COMP_7_LINEAR_SCORE
## 1 0.014 0.008 0.045
## H_HSP_RATING_LINEAR_SCORE H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
## 1 0.024 -0.002 0.061# id H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE
# mean_score -0.02 0.073 0.012 -0.017 0.045 0.014 0.008
# H_COMP_7_LINEAR_SCORE H_HSP_RATING_LINEAR_SCORE H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
# 0.045 0.024 -0.002 0.061
prov_e <- round(expe_scores[which(expe_scores$Provider.ID == 140010),], 3)
prov_e <- cbind(id = "prov_e_score", prov_e[, -1])
prov_e## id H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE
## 1125 prov_e_score 0.183 -0.129
## H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE
## 1125 -0.368 -0.29 -0.22
## H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE H_COMP_7_LINEAR_SCORE
## 1125 -0.648 -0.815 -0.183
## H_HSP_RATING_LINEAR_SCORE H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
## 1125 0.084 -0.205 0.45# id H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE
# prov_e_score 0.183 -0.129 -0.368 -0.29 -0.22 -0.648 -0.815
# H_COMP_7_LINEAR_SCORE H_HSP_RATING_LINEAR_SCORE H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
# -0.183 0.084 -0.205 0.45
average_expe_scores <- rbind(average_expe_scores, prov_e)
average_expe_scores## id H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE
## 1 mean_score -0.020 0.073
## 1125 prov_e_score 0.183 -0.129
## H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE
## 1 0.012 -0.017 0.045
## 1125 -0.368 -0.290 -0.220
## H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE H_COMP_7_LINEAR_SCORE
## 1 0.014 0.008 0.045
## 1125 -0.648 -0.815 -0.183
## H_HSP_RATING_LINEAR_SCORE H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
## 1 0.024 -0.002 0.061
## 1125 0.084 -0.205 0.450# id H_CLEAN_LINEAR_SCORE H_COMP_1_LINEAR_SCORE H_COMP_2_LINEAR_SCORE H_COMP_3_LINEAR_SCORE H_COMP_4_LINEAR_SCORE H_COMP_5_LINEAR_SCORE H_COMP_6_LINEAR_SCORE
# mean_score -0.020 0.073 0.012 -0.017 0.045 0.014 0.008
# prov_e_score 0.183 -0.129 -0.368 -0.290 -0.220 -0.648 -0.815
# H_COMP_7_LINEAR_SCORE H_HSP_RATING_LINEAR_SCORE H_QUIET_LINEAR_SCORE H_RECMND_LINEAR_SCORE
# 0.045 0.024 -0.002 0.061
# -0.183 0.084 -0.205 0.450
# The experience measures are very important.
# We notice that the provider's scores are very low compared to the average scores of H_COMP_1_LINEAR_SCORE, H_COMP_2_LINEAR_SCORE, H_COMP_3_LINEAR_SCORE,
# H_COMP_4_LINEAR_SCORE, H_COMP_5_LINEAR_SCORE, H_COMP_6_LINEAR_SCORE, H_COMP_7_LINEAR_SCORE, H_QUIET_LINEAR_SCORE.
# The scores for H_CLEAN_LINEAR_SCORE and H_RECMND_LINEAR_SCORE are very good compared to the average scores.
# Let us visualize how these low score measures are performing with respect to the ratings
# merging expe scores with all_scores
str(all_scores)## 'data.frame': 2414 obs. of 11 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score : num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id: Factor w/ 5 levels "1","2","3","4",..: 2 2 2 3 3 3 3 4 2 3 ...expe_scores <- merge(expe_scores, all_scores, by = "Provider.ID")
str(expe_scores)## 'data.frame': 2414 obs. of 22 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ H_CLEAN_LINEAR_SCORE : num -0.853 -1.112 -1.112 -1.629 -0.335 ...
## $ H_COMP_1_LINEAR_SCORE : num -0.524 -0.129 -0.129 -0.524 0.265 ...
## $ H_COMP_2_LINEAR_SCORE : num 0.0412 0.8596 0.8596 0.0412 0.8596 ...
## $ H_COMP_3_LINEAR_SCORE : num -1.1999 -0.29 -0.5175 -0.745 -0.0626 ...
## $ H_COMP_4_LINEAR_SCORE : num -0.606 0.167 -0.22 -0.606 0.167 ...
## $ H_COMP_5_LINEAR_SCORE : num -0.415 0.285 -0.182 -1.115 -0.415 ...
## $ H_COMP_6_LINEAR_SCORE : num 0.0276 0.3087 -1.0964 -0.2534 0.3087 ...
## $ H_COMP_7_LINEAR_SCORE : num 0.165 -0.183 -0.532 -0.183 -0.88 ...
## $ H_HSP_RATING_LINEAR_SCORE: num 0.0842 0.3915 -1.1451 -0.2231 0.0842 ...
## $ H_QUIET_LINEAR_SCORE : num 0.969 0.577 0.577 0.186 0.577 ...
## $ H_RECMND_LINEAR_SCORE : num 0.45 0.221 -0.923 -0.236 -0.694 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score : num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id : Factor w/ 5 levels "1","2","3","4",..: 2 2 2 3 3 3 3 4 2 3 ...# Measure: H_COMP_1_LINEAR_SCORE
summary(expe_scores$H_COMP_1_LINEAR_SCORE)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -4.8617 -0.5235 -0.1292 -0.1290 0.2652 2.2371e1 <- prov_e$H_COMP_1_LINEAR_SCORE * 100
HC1L_plot <- ggplot(expe_scores, aes(x = factor(as.character(newcluster_id)), y = H_COMP_1_LINEAR_SCORE * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("H_COMP_1_LINEAR_SCORE") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-4, 2.5, 0.5))
expe_hc1l_p <- HC1L_plot +
geom_hline(yintercept = e1, col = "black") +
geom_text(aes(0, e1, label = e1, vjust = -1, hjust = -1))
expe_hc1l_p
# The H_COMP_1_LINEAR_SCORE of the provider (-0.129) is above the ratings 1 &2 and is equal to the avaerage score of rating 3 and lower than the average score of ratings 4 & 5.
# Measure: H_COMP_2_LINEAR_SCORE
summary(expe_scores$H_COMP_2_LINEAR_SCORE)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -4.46010 -0.77724 0.04118 -0.18292 0.45038 2.49642e2 <- prov_e$H_COMP_2_LINEAR_SCORE * 100
HC2L_plot <- ggplot(expe_scores, aes(x = factor(as.character(newcluster_id)), y = H_COMP_2_LINEAR_SCORE * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("H_COMP_2_LINEAR_SCORE") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-4, 2.5, 0.5))
expe_hc2l_p <- HC2L_plot +
geom_hline(yintercept = e2, col = "black") +
geom_text(aes(0, e2, label = e2, vjust = -1, hjust = -1))
expe_hc2l_p
# The H_COMP_2_LINEAR_SCORE of the provider is above the ratings 1 &2 and is equal to the avaerage score of rating 3 and lower than the average score of ratings 4 & 5.
# Measure: H_COMP_3_LINEAR_SCORE
summary(expe_scores$H_COMP_3_LINEAR_SCORE)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -4.30501 -0.74495 -0.06257 -0.20950 0.39235 2.43949e3 <- prov_e$H_COMP_3_LINEAR_SCORE * 100
HC3L_plot <- ggplot(expe_scores, aes(x = factor(as.character(newcluster_id)), y = H_COMP_3_LINEAR_SCORE * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("H_COMP_3_LINEAR_SCORE") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-4, 2.5, 0.5))
expe_hc3l_p <- HC3L_plot +
geom_hline(yintercept = e3, col = "black") +
geom_text(aes(0, e3, label = e3, vjust = -1, hjust = -1))
expe_hc3l_p
# The H_COMP_3_LINEAR_SCORE of the provider is above the ratings 1 &2 and is equal to the avaerage score of rating 3 and lower than the average score of ratings 4 & 5.
# Measure: H_COMP_4_LINEAR_SCORE
summary(expe_scores$H_COMP_4_LINEAR_SCORE)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -4.4719 -0.6064 -0.2198 -0.1337 0.5533 2.4860e4 <- prov_e$H_COMP_4_LINEAR_SCORE * 100
HC4L_plot <- ggplot(expe_scores, aes(x = factor(as.character(newcluster_id)), y = H_COMP_4_LINEAR_SCORE * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("H_COMP_4_LINEAR_SCORE") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-4, 2.5, 0.5))
expe_hc4l_p <- HC4L_plot +
geom_hline(yintercept = e4, col = "black") +
geom_text(aes(0, e4, label = e4, vjust = -1, hjust = -1))
expe_hc4l_p
# Measure: H_COMP_5_LINEAR_SCORE
summary(expe_scores$H_COMP_5_LINEAR_SCORE)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3.9139 -0.6485 -0.1820 -0.1808 0.2845 3.0834e5 <- prov_e$H_COMP_5_LINEAR_SCORE * 100
HC5L_plot <- ggplot(expe_scores, aes(x = factor(as.character(newcluster_id)), y = H_COMP_5_LINEAR_SCORE * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("H_COMP_5_LINEAR_SCORE") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-4, 2.5, 0.5))
expe_hc5l_p <- HC5L_plot +
geom_hline(yintercept = e5, col = "black") +
geom_text(aes(0, e5, label = e5, vjust = -1, hjust = -1))
expe_hc5l_p
# The H_COMP_5_LINEAR_SCORE of the provider is above the ratings 1 and is equal to the avaerage score of rating 2 and lower than the average score of ratings 3, 4 & 5.
# Measure: H_COMP_6_LINEAR_SCORE
summary(expe_scores$H_COMP_6_LINEAR_SCORE)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -4.55368 -0.53440 0.02764 -0.05276 0.58967 2.27577e6 <- prov_e$H_COMP_6_LINEAR_SCORE * 100
HC6L_plot <- ggplot(expe_scores, aes(x = factor(as.character(newcluster_id)), y = H_COMP_6_LINEAR_SCORE * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("H_COMP_6_LINEAR_SCORE") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-4, 2.5, 0.5))
expe_hc6l_p <- HC6L_plot +
geom_hline(yintercept = e6, col = "black") +
geom_text(aes(0, e6, label = e6, vjust = -1, hjust = -1))
expe_hc6l_p
# The H_COMP_6_LINEAR_SCORE of the provider is above the ratings 1 and lower than the average score of ratings 2, 3, 4 & 5.
# Measure: H_COMP_7_LINEAR_SCORE
summary(expe_scores$H_COMP_7_LINEAR_SCORE)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -4.0139 -0.5316 -0.1833 -0.1236 0.5131 3.6472e7 <- prov_e$H_COMP_7_LINEAR_SCORE * 100
HC7L_plot <- ggplot(expe_scores, aes(x = factor(as.character(newcluster_id)), y = H_COMP_7_LINEAR_SCORE * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("H_COMP_7_LINEAR_SCORE") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-4, 2.5, 0.5))
expe_hc7l_p <- HC7L_plot +
geom_hline(yintercept = e7, col = "black") +
geom_text(aes(0, e7, label = e7, vjust = -1, hjust = -1))
expe_hc7l_p
# The H_COMP_7_LINEAR_SCORE of the provider is above the ratings 1 &2 and is equal to the avaerage score of rating 3 and lower than the average score of ratings 4 & 5.
# Measure: H_QUIET_LINEAR_SCORE
summary(expe_scores$H_QUIET_LINEAR_SCORE)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3.463444 -0.792407 -0.009761 -0.187534 0.381562 1.946854e8 <- prov_e$H_QUIET_LINEAR_SCORE * 100
HQL_plot <- ggplot(expe_scores, aes(x = factor(as.character(newcluster_id)), y = H_QUIET_LINEAR_SCORE * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("H_QUIET_LINEAR_SCORE") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-3, 2, 0.5))
expe_hql_p <- HQL_plot +
geom_hline(yintercept = e8, col = "black") +
geom_text(aes(0, e8, label = e8, vjust = -1, hjust = -1))
expe_hql_p
# The H_QUIET_LINEAR_SCORE of the provider is above the ratings 1 &2 and is equal to the avaerage score of rating 3 and lower than the average score of ratings 4 & 5.
grid_plot5 <- plot_grid(expe_hc1l_p, expe_hc2l_p, expe_hc3l_p, expe_hc4l_p, expe_hc5l_p, expe_hc6l_p, expe_hc7l_p, expe_hql_p,
labels = c('H_COMP_1_LINEAR_SCORE', 'H_COMP_2_LINEAR_SCORE', 'H_COMP_3_LINEAR_SCORE', 'H_COMP_4_LINEAR_SCORE',
'H_COMP_5_LINEAR_SCORE', 'H_COMP_6_LINEAR_SCORE', 'H_COMP_7_LINEAR_SCORE', 'H_QUIET_LINEAR_SCORE'),
ncol = 3, nrow = 3)
grid_plot5
3.Medical
#———–#
# we will plot the provider score in the y intercept.
h3 <- provider_140010_gp_scores$medi_score * 100
medi_plot +
geom_hline(yintercept = h3, col = "maroon") +
geom_text(aes(0, h3, label = h3, vjust = -1, hjust = -1))
medi_scores <- medi_master[, -c(2:8)]
summary(medi_scores)## Provider.ID OP_10_score OP_11_score OP_13_score
## Min. : 10001 Min. :-6.5644 Min. :-8.0024 Min. :-3.9422
## 1st Qu.:140185 1st Qu.:-0.1401 1st Qu.:-0.0394 1st Qu.:-0.5537
## Median :260037 Median : 0.3191 Median : 0.3886 Median : 0.0428
## Mean :267984 Mean : 0.0005 Mean : 0.0011 Mean : 0.0010
## 3rd Qu.:390209 3rd Qu.: 0.5634 3rd Qu.: 0.5256 3rd Qu.: 0.6394
## Max. :670112 Max. : 0.8761 Max. : 0.5427 Max. : 2.3296
## NA's :1189 NA's :1469 NA's :2585
## OP_14_score OP_8_score
## Min. :-5.4208 Min. :-3.141
## 1st Qu.:-0.6490 1st Qu.:-0.652
## Median : 0.1540 Median : 0.098
## Mean : 0.0005 Mean : 0.000
## 3rd Qu.: 0.6893 3rd Qu.: 0.660
## Max. : 1.4922 Max. : 2.880
## NA's :2514 NA's :3294medi_scores[, 2:ncol(medi_scores)] <- sapply(medi_scores[, -1], median_na)
summary(medi_scores)## Provider.ID OP_10_score OP_11_score OP_13_score
## Min. : 10001 Min. :-6.56436 Min. :-8.0024 Min. :-3.94217
## 1st Qu.:140185 1st Qu.: 0.06509 1st Qu.: 0.2003 1st Qu.: 0.04281
## Median :260037 Median : 0.31913 Median : 0.3886 Median : 0.04281
## Mean :267984 Mean : 0.07911 Mean : 0.1193 Mean : 0.02344
## 3rd Qu.:390209 3rd Qu.: 0.48524 3rd Qu.: 0.4743 3rd Qu.: 0.04281
## Max. :670112 Max. : 0.87606 Max. : 0.5427 Max. : 2.32955
## OP_14_score OP_8_score
## Min. :-5.42084 Min. :-3.14132
## 1st Qu.: 0.15397 1st Qu.: 0.09767
## Median : 0.15397 Median : 0.09767
## Mean : 0.08057 Mean : 0.06667
## 3rd Qu.: 0.15397 3rd Qu.: 0.09767
## Max. : 1.49221 Max. : 2.87987# The medical scores for the provider
provider_medi_scores <- round(medi_scores[which(medi_scores$Provider.ID == 140010),], 3)
provider_medi_scores## Provider.ID OP_10_score OP_11_score OP_13_score OP_14_score
## 1125 140010 0.261 0.2 -0.454 0.315
## OP_8_score
## 1125 0.285# Provider.ID OP_10_score OP_11_score OP_13_score OP_14_score OP_8_score
# 140010 0.261 0.2 -0.454 0.315 0.285
# Let us calculate the average scores and compare them
average_medi_scores <- round(summarise_all(medi_scores[, -1], .funs = (mean), na.rm = T), 3)
average_medi_scores <- cbind(id = "mean_score", average_medi_scores)
average_medi_scores## id OP_10_score OP_11_score OP_13_score OP_14_score OP_8_score
## 1 mean_score 0.079 0.119 0.023 0.081 0.067# id OP_10_score OP_11_score OP_13_score OP_14_score OP_8_score
# mean_score 0.079 0.119 0.023 0.081 0.067
prov_m <- round(medi_scores[which(medi_scores$Provider.ID == 140010),], 3)
prov_m <- cbind(id = "prov_m_score", prov_m[, -1])
prov_m## id OP_10_score OP_11_score OP_13_score OP_14_score
## 1125 prov_m_score 0.261 0.2 -0.454 0.315
## OP_8_score
## 1125 0.285# id OP_10_score OP_11_score OP_13_score OP_14_score OP_8_score
# prov_m_score 0.261 0.2 -0.454 0.315 0.285
average_medi_scores <- rbind(average_medi_scores, prov_m)
average_medi_scores## id OP_10_score OP_11_score OP_13_score OP_14_score
## 1 mean_score 0.079 0.119 0.023 0.081
## 1125 prov_m_score 0.261 0.200 -0.454 0.315
## OP_8_score
## 1 0.067
## 1125 0.285# id OP_10_score OP_11_score OP_13_score OP_14_score OP_8_score
# mean_score 0.079 0.119 0.023 0.081 0.067
# prov_m_score 0.261 0.200 -0.454 0.315 0.285
# The Medical group have only 4% weightage.
# We see that the provider's scores are very high compared to average scores of all the measures, except OP_13_score.
# OP_13_score has very low score compared to the average score.
# We will check how the scores for this column are.
# Let us visualize how these low score measures are performing with respect to the ratings
# merging medi scores with all_scores
str(all_scores)## 'data.frame': 2414 obs. of 11 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score : num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id: Factor w/ 5 levels "1","2","3","4",..: 2 2 2 3 3 3 3 4 2 3 ...medi_scores <- merge(medi_scores, all_scores, by = "Provider.ID")
str(medi_scores)## 'data.frame': 2414 obs. of 16 variables:
## $ Provider.ID : int 10001 10005 10006 10011 10012 10016 10019 10021 10023 10024 ...
## $ OP_10_score : num 0.251 -0.423 -0.277 0.642 0.28 ...
## $ OP_11_score : num 0.389 -1.204 -0.245 -1.238 -1.306 ...
## $ OP_13_score : num -1.2 -0.3052 2.3296 0.0428 0.7885 ...
## $ OP_14_score : num 0.207 -0.649 -0.97 -0.328 0.154 ...
## $ OP_8_score : num 0.2996 -0.3783 -0.7822 0.5737 0.0977 ...
## $ radm_score : num -0.04 0.002 0.006 0.069 -0.013 0.004 -0.056 0.007 -0.085 0.009 ...
## $ mort_score : num -0.021 -0.164 -0.182 -0.009 -0.162 -0.069 -0.069 0.037 -0.114 -0.087 ...
## $ safety_score : num -0.104 -0.005 -0.047 -0.052 0.06 0.032 0.033 0.04 -0.014 -0.075 ...
## $ expe_score : num -0.033 -0.017 -0.039 -0.03 0.017 -0.007 -0.021 0.023 -0.004 0.026 ...
## $ medi_score : num 0.033 -0.07 0.137 -0.08 -0.112 -0.271 -0.101 -0.042 -0.031 0.089 ...
## $ time_score : num -0.043 0.071 -0.006 -0.229 0.075 -0.048 -0.042 0.082 -0.031 0.035 ...
## $ effe_score : num 0.008 0.022 0.017 -0.088 0.009 0.023 -0.01 0.024 0.014 -0.01 ...
## $ final_score : num -0.046 -0.04 -0.052 -0.014 -0.024 -0.021 -0.033 0.025 -0.054 -0.023 ...
## $ cluster_id : int 3 3 3 5 5 5 5 4 3 5 ...
## $ newcluster_id: Factor w/ 5 levels "1","2","3","4",..: 2 2 2 3 3 3 3 4 2 3 ...# Measure: OP_13_score
summary(medi_scores$OP_13_score)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3.94217 -0.40460 0.04281 -0.01064 0.44050 2.32955m1 <- prov_m$OP_13_score * 100
OP13_plot <- ggplot(medi_scores, aes(x = factor(as.character(newcluster_id)), y = OP_13_score * 100, fill = newcluster_id)) +
geom_boxplot() +
xlab("Star Ratings") +
ylab("OP_13_score") +
theme_light() +
scale_y_continuous(breaks = 100 * seq(-4, 2.5, 0.5))
medi_op13_p <- OP13_plot +
geom_hline(yintercept = m1, col = "black") +
geom_text(aes(0, m1, label = m1, vjust = -1, hjust = -1))
medi_op13_p
OP_13 measure value is very low compared to the average score accorss the ratings.
If the provider fix’s this issue the medical scores will drastically improve.
Summary of provider analysis:
The provider has low scores in the groups safety, patient-experience and medical.
The safety score of provider is -2.2 (on the scale of this plot safe_plot)
The experience score is -0.2 (on this scale expe_plot)
The medical score is -1.3 (on this scale medi_plot)
1.Safety Observations:
The provider’s score for COMP_HIP_KNEE_score is way below the average scores for all the ratings.
This is a cause of concern, the provider has to improve on this area to improve their ratings.
Rate of complications for hip/knee replacement patients
Provider needs to take measures to simplify the procedure involved in the hip/knee replacement operations.
HAI_2_SIR score of the provider is lower than the average scores for ratings 3, 4, 5 and above the average scores for ratings 1 & 2.
Catheter associated urinary tract infections (CAUTI) in ICUs and select wards
Provider needs to take measures to protect the patients from contracting these infections.
HAI_3_SIR score of the provider is way below the average scores for all the ratings.
Surgical Site Infection from colon surgery (SSI: Colon)
Provider needs to take precuations to avoid these infections which are very sensitive and might lead to other severe problems for the patients.
Provider needs to take measures to protect the patients from contracting these infections.
HAI_6_SIR score of the provider is way below the average scores for all the ratings.
Clostridium difficile (C.diff.) Laboratory identified Events (Intestinal Infections)
Provider needs to take measures to protect the patients from contracting these infections.
PSI_90_SAFETY score of the provider is way below the average scores for all the ratings.
The provider is doing poorly in this measure.
Serious complications, provider needs to take effort to resolve these safety issues which the
patients are contracting due to various procedures.
2.Experience Observations:
The experience measures are very important.
We notice that the provider’s scores are very low compared to the average scores of H_COMP_1_LINEAR_SCORE, H_COMP_2_LINEAR_SCORE, H_COMP_3_LINEAR_SCORE,
H_COMP_4_LINEAR_SCORE, H_COMP_5_LINEAR_SCORE, H_COMP_6_LINEAR_SCORE, H_COMP_7_LINEAR_SCORE, H_QUIET_LINEAR_SCORE.
The scores for H_CLEAN_LINEAR_SCORE and H_RECMND_LINEAR_SCORE are very good compared to the average scores.
The H_COMP_1_LINEAR_SCORE of the provider (-0.129) is above the ratings 1 &2 and is equal to the avaerage score of rating 3 and lower than the average score of ratings 4 & 5.
Nurse communication linear mean score - provider needs to work on the communication issues regarding Nurses.
The H_COMP_2_LINEAR_SCORE of the provider is above the ratings 1 &2 and is equal to the avaerage score of rating 3 and lower than the average score of ratings 4 & 5.
Doctor communication linear mean score - provider needs to work on the communication issues regarding Doctors.
The H_COMP_3_LINEAR_SCORE of the provider is above the ratings 1 &2 and is equal to the avaerage score of rating 3 and lower than the average score of ratings 4 & 5.
Staff responsiveness linear mean score - provider needs to work on the communication issues regarding Doctors.
The H_COMP_5_LINEAR_SCORE of the provider is above the ratings 1 and is equal to the avaerage score of rating 2 and lower than the average score of ratings 3, 4 & 5.
Communication about medicines linear mean score - provider needs to ensure the information regarding the medicines properly and clearly.
The H_COMP_6_LINEAR_SCORE of the provider is above the ratings 1 and lower than the average score of ratings 2, 3, 4 & 5.
Discharge information linear mean score - provider needs to ensure the discharge information is provided clearly to the patients.
The H_COMP_7_LINEAR_SCORE of the provider is above the ratings 1 &2 and is equal to the avaerage score of rating 3 and lower than the average score of ratings 4 & 5.
Care transition linear mean score
The H_QUIET_LINEAR_SCORE of the provider is above the ratings 1 &2 and is equal to the avaerage score of rating 3 and lower than the average score of ratings 4 & 5.
Quietness linear mean score - needs to improve on the noise management in the hospital areas.
Overall, in the experience measures the provider has to improve on the communications of Nurses/Doctos/Staff/Medicines/Quietness and Care transition.
3.Medical Observations:
The Medical group have only 4% weightage.
We see that the provider’s scores are very high compared to average scores of all the measures, except OP_13_score.
OP_13_score has very low score compared to the average score.
We will check how the scores for this column are.
OP_13 measure value is very low compared to the average score accorss the ratings.
Outpatients who got cardiac imaging stress tests before low risk outpatient surgery - provider needs to avoid these unnecessary tests and follow the protocol standards.
If the provider fix’s this issue the medical scores will drastically improve.
Code ENDS